Anomaly diagnosis apparatus and method of machine installation

ABSTRACT

An object of the invention is to provide an anomaly diagnosis apparatus of a machine installation for eliminating the need for the user to have a dedicated analytical instrument or a skill required for anomaly diagnosis of a sliding member and enabling the user to easily make an anomaly diagnosis request with a small burden and get the diagnosis result promptly.  
     In the invention, an anomaly diagnosis apparatus for analyzing sound or vibration data produced by a machine installation and diagnosing the presence or absence of an anomaly in a sliding member in the machine installation is made up of a diagnosis processing server ( 1 ) and a user information processing terminal ( 3 ) which are connected through a network ( 2 ). The diagnosis processing server ( 1 ) receives the sound or vibration data produced by the machine installation and information for identifying the sliding member used with the machine installation through the network ( 2 ) from the user information processing terminal  3 , makes an anomaly diagnosis of the machine installation based on the received data, and transmits the diagnosis result through the network ( 2 ) to the user information processing terminal ( 3 ), thereby lightening the burden of the user and executing anomaly diagnosis processing promptly.

TECHNICAL FIELD

This invention relates to an anomaly diagnosis apparatus and method of amachine installation using a network for diagnosing the presence orabsence of an anomaly in a sliding member used with a machineinstallation.

BACKGROUND ART

A system for detecting sound or vibration produced from a machineinstallation using a sliding member as a vibration element, performingprocessing of frequency analysis, etc., for detected data, and detectingan anomaly and estimating the cause of the anomaly, etc., in the slidingmember used with the machine installation based on obtained analysisdata is well known.

By the way, the sliding members used with the machine installationinclude bearings, ball screws, linear guides, motors, etc., for example;the sliding members involve a large number of types and are used atvarious places and in various environments.

Therefore, the factors of sound or vibration caused by the slidingmembers are complicated and to analyze the factors and make a highlyreliable anomaly diagnosis, usually a dedicated analytical instrumentand a skill for making a diagnosis become necessary. However, usuallythe user of the machine installation does not have such a skill requiredfor making a diagnosis or a dedicated analytical instrument.

Then, whenever the user wants to conduct an anomaly diagnosis of asliding member in a routine check, etc., for example, the user passesdata of sound or vibration produced by the machine installation to besubjected to a diagnosis to a machine installation manufacturer, etc.,having a necessary skill and a dedicated analytical instrument, andmakes a diagnosis request.

FIG. 46 shows a related art example of an anomaly diagnosis apparatus ofa machine installation placed in a machine installation manufacturer,etc., as a dedicated analytical instrument for diagnosing the presenceor absence of an anomaly in a sliding member.

An anomaly diagnosis apparatus 901 shown here assumes that a slidingmember built in a machine installation is a rolling bearing made up of aplurality of parts for rotating and sliding.

As the anomaly diagnosis apparatus 901 of a machine installation, adiagnosis program 905 and determination criterion data 907 for thediagnosis are built in an information processing apparatus (computer)903 managed by the manufacturer, etc., designing the machineinstallation and when a user 909 using the machine installation requeststhe manufacturer to diagnose the presence or absence of an anomaly inthe machine installation, the manufacturer processes actual measurementvibration data recording sound or vibration occurring at the operationtime of the machine installation of the user 909 by the diagnosisprogram 905 of the information processing apparatus 903 for diagnosingthe presence or absence of an anomaly, and responds to the user 909 withthe diagnosis result.

As the determination criterion data 907, the frequency component ofvibration occurring when a specific part of the sliding member used withthe machine installation is abnormal and the standard peak level in thefrequency component are calculated from the specifications, etc., of thesliding member and are set; the determination criterion data 907 isstored in a data storage section 911 of the information processingapparatus 903.

Usually, for example, various databases useful for diagnosis processing,such as a sound/vibration database 971 storing the determinationcriterion data 907, a customer information database 981 storinginformation concerning the users using the machine installations to bediagnosed, and a measure database 991 storing information of measures,etc., returned to the user in response to the abnormal condition, areconstructed in the data storage section 911.

The diagnosis program 905 is made up of an actual measurement dataanalysis program 912 for performing appropriate analysis processing ofenvelope analysis, etc., for the actual measurement vibration dataobtained from the user and generating actual measurement frequencyspectrum data, for example, and a determination program 913 fordiagnosing the presence or absence of an anomaly by comparing theanalysis result of the actual measurement data analysis program 912 withthe determination criterion data 907.

The determination program 913 adopts as the data stored in thedetermination criterion data 907 the frequency component occurring whena specific part of the machine installation is abnormal as thedetermination criterion, for example, and determines the presence orabsence of an anomaly in the machine installation and locates theanomaly occurrence point based on whether or not a peak value of a givenlevel or more appears at the determination criterion position on theactual measurement frequency spectrum data of the analysis result of theactual measurement data analysis program 912.

However, in the related art, the data required for a diagnosis istransferred by mail, etc., or in both the user and the manufacturer,time and labor of the persons in charge, etc., are required for sendingand receiving an anomaly diagnosis request and therefore the diagnosisresult cannot promptly be provided; this is a problem.

Since sound or vibration which occurs varies largely depending on thespecification data and use conditions of the sliding member used withthe machine installation, it becomes necessary to enter thespecification data and use conditions to make a precise diagnosis.

However, it is not easy for the user to prepare a document describingthe specification data, use conditions, etc., whenever the user makes ananomaly diagnosis request, and arrangements for the data and useconditions required for a diagnosis place a large burden on the user,resulting in a problem of making it impossible to easily make an anomalydiagnosis.

Further, the anomaly diagnosis apparatus 901 in the related artdescribed above involves the following problems:

As processing of the determination program 913, the anomaly diagnosisapparatus 901 in the related art described above repeats computationprocessing of finding a large number of frequency components at theanomaly time from the first order to multi-order for each specific partof the machine installation predetermined and making a diagnosis as towhether or not a peak appears on the frequency spectrum data of thesliding member of the machine installation actually measured for each ofthe many frequency components and computation processing of making adiagnosis as to whether or not the peak value on the frequency spectrumdata is a peak level corresponding to an anomaly.

Thus, the computation processing amount until the final diagnosis isreached becomes enormous, and a large load is imposed on computationprocessing means and thus an expensive computer having a highcomputation processing capability becomes necessary, resulting in anincrease in the apparatus cost and as the required time for computationprocessing is prolonged, it becomes difficult to speed up the diagnosiswork; this is a problem.

To finish diagnosis processing of a plurality of users promptly as muchas possible, a high-performance computer having a high computationprocessing capability becomes necessary as the information processingapparatus 903, and a problem of increasing the construction cost of acomputer system as the diagnosis apparatus occurs.

If user's diagnosis requests concentrate, starting of handling of somerequests is delayed and thus the required time until the final diagnosisis reached is further increased and even if a computer having a highcomputation processing capability is adopted as the informationprocessing apparatus 903, a problem of making it difficult to make arapid response also occurs.

Further, the user needs to transmit actual measurement vibration data tothe manufacturer and the time required for transmitting the actualmeasurement vibration data from the user to the manufacturer alsobecomes a factor causing a delay in diagnosis processing.

A method of repeating computation processing of checking to see if eachfrequency spectrum after undergoing frequency analysis involves afrequency component caused by a sliding member of a machine installationin the descending order of spectrum level without picking up the peakvalue, etc., and without determining the frequency at which a peakoccurs due to an anomaly, etc., also becomes widespread as thecomparison processing of the determination program in the related art todetect an anomaly.

In such a method, however, calculation load and time loss are heavy,resulting in a delay in processing; this is a problem. Although ananomaly occurs, if a peak appearing in the frequency spectrum is small,the anomaly is missed and overlooked or when the spectrum level is highbecause of the effect of noise, if it does not correspond to the peakvalue, there is a fear of an erroneous diagnosis as an anomaly, and itis difficult to improve reliability of diagnosis; this is also aproblem.

In the anomaly diagnosis method in the related art, if noise is put onthe harmonic of a frequency component caused by an anomaly in thesliding member of the machine installation or a member relevant to thesliding member of the machine installation, if frequency components ofrotation components, etc., in the sliding member or the member relevantto the sliding member overlap, or if the harmonic of a frequencycomponent not caused by the anomaly in the sliding member or the memberrelevant to the sliding member and the frequency component caused by theanomaly overlap, there is a fear of an erroneous diagnosis as an anomalybecause of the effect of the harmonic if actually the sliding member,the member relevant to the sliding member, etc., is normal.

Then, in the related art, the person in charge of diagnosis for managingthe anomaly diagnosis apparatus of the machine installation checksactual measurement data provided by vibration detection means each time,extracts a region in which a noise component seems to be small from theactual measurement data provided by the vibration detection means, andexecutes conversion processing to frequency spectrum data and latercomparison processing for the extracted effective actual measurementdata, thereby preventing degradation of reliability of diagnosis.

The person in charge of diagnosis checks to see if a noise componentexists, for example, by visually checking whether or not a given or morepeak value appears on the vibration waveform detected by the vibrationdetection means.

Noise is detected by the vibration detection means in a state in whichit is added to sound or vibration occurring in the machine installationcontaining the sliding member, and often makes excessive the peak levelof the waveform.

However, removal of the noise component by the person in charge ofdiagnosis interrupts processing of the anomaly diagnosis apparatus ofthe machine installation and thus incurs a drastic delay in thediagnosis processing; this is a problem.

An output unit is required for displaying the actual measurement datadetected by the vibration detection means in such a manner that theperson in charge of diagnosis can check the actual measurement data, andthere is a problem of increasing the cost of the anomaly diagnosisapparatus of the machine installation to install the output unit.

Further, the noise component removal percentage is affected by the skilldegree of the person in charge of diagnosis and variations easily occurin reliability of diagnosis; this is also a problem.

The invention is embodied considering the problems described above andit is an object of the invention to provide an anomaly diagnosisapparatus of a machine installation for enabling the user to easily makean anomaly diagnosis request with a small burden and moreover get thediagnosis result promptly and deal with occurrence of the anomalyrapidly if the user does not have a dedicated analytical instrument or askill required for anomaly diagnosis of a sliding member, further toprovide an anomaly diagnosis apparatus of a machine installation forenabling diagnosis processing to be performed in any desired informationprocessing terminal owned by the user for speeding up the diagnosisprocessing, and further to provide an anomaly diagnosis method of amachine installation for making it possible to decrease the calculationload for anomaly diagnosis and detect the presence or presence of ananomaly in a short time with good accuracy.

DISCLOSURE OF THE INVENTION

To accomplish the object, according to the invention, there is provided

(1) an anomaly diagnosis apparatus of a machine installation fordiagnosing the presence or absence of an anomaly in a sliding memberused with the machine installation using sound, vibration, ortemperature produced from the machine installation, the anomalydiagnosis apparatus having:

a diagnosis processing server and the user information processingterminal connected to a network, characterized in that

the user information processing terminal transmits sound, vibration, ortemperature data produced from one or more sliding members used with themachine installation and information for identifying the one or moresliding members to the diagnosis processing server, and that

the diagnosis processing server makes an anomaly diagnosis of themachine installation based on the transmitted sound, vibration, ortemperature data and specification data of the one or more slidingmembers based on the information for identifying them, and transmits thediagnosis result to the user information processing terminal.

The information transmitted by the user information processing terminalto the diagnosis processing server as the information for identifyingthe sliding members also includes use condition information of the oneor more sliding members. The diagnosis processing server performsanomaly diagnosis processing considering the use condition informationof the sliding members received from the user information processingterminal.

The anomaly diagnosis conducted by the diagnosis processing serverincludes determination of the presence or absence of an anomaly in themachine installation and estimation of the anomaly cause.

For example, bearings, ball screws, linear guides, motors, etc., comeunder the sliding members.

In the described anomaly diagnosis apparatus of the machineinstallation, when the user of the machine installation wants diagnosisof the presence or absence of an anomaly in the sliding member used withthe machine installation, if the user transmits the sound or vibrationdata at the use point of the sliding member on the machine installationrequired for the anomaly diagnosis, the information identifying thesliding member, the sliding member use condition information, etc., fromthe user information processing terminal through the network to thediagnosis processing server, the diagnosis processing serverautomatically executes anomaly diagnosis processing based on thereceived data and further transmits the diagnosis result through thenetwork to the user information processing terminal.

To execute periodic anomaly diagnosis, for example, for one or moresliding members on the machine installation, if the use conditions ofthe sliding member and the information for identifying the slidingmember transmitted by the user to the diagnosis processing server areonce prepared and stored in a storage unit, etc., of the userinformation processing terminal, unless the information is changed, itcan be used repeatedly, similar information need not be prepared fromthe beginning each time an anomaly diagnosis request is made, and theburden required for preparing information required for making an anomalydiagnosis request can be lightened drastically.

Further, an anomaly diagnosis request is sent directly to the diagnosisprocessing server that can automatically execute anomaly diagnosisprocessing not via a window job for the person in charge to accept therequest manually, and the requested diagnosis processing is executedpromptly within the scope of the information processing performance ofthe diagnosis processing server, so that the user can get the diagnosisresult early.

To accomplish the object, according to the invention, there is provided

(2) an anomaly diagnosis apparatus of a machine installation wherein anactual measurement data analysis program for analyzing actualmeasurement vibration data recording sound or vibration produced when asliding member used with the machine installation operates,determination criterion data recording information used as adetermination criterion of the presence or absence of an anomaly in thesliding member used with the machine installation, and a determinationprogram for comparing the analysis result of the actual measurement dataanalysis program with the determination criterion data and diagnosingthe presence or absence of an anomaly in the sliding member arepreviously uploaded to a diagnosis processing server connected to anetwork in an executable data format in an information processingterminal of the user using the machine installation so as to enable theprograms and the data to be downloaded into the user informationprocessing terminal, characterized in that

the user of the machine installation downloads the actual measurementdata analysis program, the determination program, and the determinationcriterion data through the network into the user's informationprocessing terminal, inputs the actual measurement vibration datathrough an interface to the user's information processing terminalwhenever necessary, and executes the actual measurement data analysisprogram and the determination program in the user's informationprocessing terminal for diagnosing the presence or absence of an anomalyin the sliding member used with the machine installation in the user'sinformation processing terminal.

The machine installation contains one or more sliding members and meansa machine installation or a machine wherein vibration occurs as thesliding members slide. The sliding members also include ball screws,linear guides, motors, etc., for example, in addition to rollingbearings.

In the described anomaly diagnosis apparatus of the machineinstallation, to diagnose the presence or absence of an anomaly in themachine installation, the diagnosis processing of the diagnosis programis performed in the information processing terminal installed in theuser, so that the user is saved from having to transmit the actualmeasurement vibration data recording sound or vibration produced by themachine installation to be diagnosed to the manufacturer, and labor andtime required for transmitting the actual measurement vibration data tothe manufacturer can be saved.

The diagnosis processing server to which the actual measurement dataanalysis program, the determination program, and the determinationcriterion data required for the diagnosis processing are uploaded isused to download the programs and the determination criterion data anddoes not execute the diagnosis processing itself and thereforeconcentrating of the diagnosis processing of a large number of users onone information processing apparatus can be circumvented.

Further, the actual measurement data analysis program, the determinationprogram, and the determination criterion data required for the diagnosisprocessing are downloaded into the information processing terminal ofthe user via the network and can be introduced into any desiredinformation processing terminal of the user if the informationprocessing terminal has a predetermined communication function andprogram execution performance, and the diagnosis processing can be leftto any idle information processing apparatus of the user.

Further, concentrating of the diagnosis processing of a large number ofusers on one information processing apparatus need not be considered asdescribed early, so that it can be expected that even an informationprocessing apparatus having a not so high computation processingcapability will perform comparatively rapid processing.

(3) To accomplish the object, in the anomaly diagnosis apparatus of themachine installation described above in (2), the Internet is used as thenetwork.

In the described anomaly diagnosis apparatus of the machineinstallation, the Internet already constructed as a wide-area networkand also promoted to broadband for realizing high-speed communicationsis used as the network for downloading, so that there is no extra costfor constructing, improving, etc., a dedicated network and a largenumber of users can use the diagnosis system easily and at low cost.

(4) To accomplish the object, in the anomaly diagnosis apparatus of themachine installation described above in (2) or (3), an authenticationprogram for comparing with customer information data of informationrequired for authenticating the user using the machine installation andallowing the user to download the actual measurement data analysisprogram, the determination program, and the determination criterion datawhen authenticating the user accessing the diagnosis processing serveras the authorized user is built in the diagnosis processing server.

In the described anomaly diagnosis apparatus of the machineinstallation, it is made possible for the diagnosis processing server tomanage the versions, etc., of the diagnosis program and thedetermination criterion data required by the user in more detail withthe authentication program and the customer data in association witheach other, for example; it is made possible to realize reliabledownloading the optimum diagnosis program and the determinationcriterion data without placing any burden on the user accessing thediagnosis processing server.

(5) To accomplish the object, in the anomaly diagnosis apparatus of themachine installation described above in any of (2) to (4), a protectprogram for limiting the number of use times or the expiration date inthe downloading information processing terminal is added to the programsand the data downloaded from the diagnosis processing server into theuser information processing terminal.

In the described anomaly diagnosis apparatus of the machineinstallation, illegal repeated use or drain of the diagnosis processingprogram and the determination criterion data downloaded into the usercan be prevented and erroneous use of the program and the data foranomaly diagnosis of a different type of machine installation or thelike can be prevented.

To accomplish the object, according to the invention, there is provided

(6) an anomaly diagnosis method of a machine installation for diagnosingthe presence or absence of an anomaly in a sliding member used with themachine installation by analyzing sound or vibration produced from themachine installation, characterized by:

detecting a signal representing sound or vibration from the slidingmember of the machine installation or a member relevant to the slidingmember of the machine installation;

finding a frequency spectrum of the detected signal or an envelopesignal thereof; and

extracting only a frequency component caused by an anomaly in thesliding member of the machine installation or the member relevant to thesliding member of the machine installation from the found frequencyspectrum and diagnosing the presence or absence of an anomaly in thesliding member used with the machine installation based on the magnitudeof the extracted frequency component.

The anomaly diagnosis method of the machine installation according tothe invention is characterized by the fact that

(7) in the anomaly diagnosis method described above in (6), the presenceor absence of an anomaly is diagnosed by comparing with a referencevalue determined in response to the effective value of the detectedsignal or the envelope signal thereof.

In the described anomaly diagnosis method of the machine installation,only specific frequency components are extracted from the frequencyspectrum of an envelope signal of vibration occurring from the bearingand are compared with the reference value determined in response to theeffective value of the envelope signal, so that the computationprocessing amount is lessened and speeding up the diagnosis processingcan be promoted as compared with the related art method of checking tosee if each frequency component is caused by the sliding member of themachine installation in the descending order of spectrum levels afterfrequency analysis. If the spectrum level peak of the frequencycomponent caused by the sliding member of the machine installation issmall, the components can be extracted and thus a highly accuratediagnosis is made possible.

To accomplish the object, according to the invention, there is provided

(8) an anomaly diagnosis method of a machine installation for detectingsound or vibration produced from a sliding member of the machineinstallation or a member relevant to the sliding member of the machineinstallation, analyzing a detection signal, and diagnosing the presenceor absence of an anomaly caused by the sliding member of the machineinstallation or the member relevant to the sliding member of the machineinstallation, characterized by:

converting an analog signal of sound or vibration produced from thesliding member of the machine installation or the member relevant to thesliding member of the machine installation into digital form to generateactual measurement digital data, performing appropriate analysisprocessing of frequency analysis, envelope analysis, and the like forthe generated actual measurement digital data to generate actualmeasurement frequency spectrum data, calculating the level differencebetween each arbitrary data point and its immediately preceding datapoint and a gradient to find a peak value for the generated actualmeasurement frequency spectrum data, and comparing a peak value on theactual measurement frequency spectrum data for the frequency componentcaused by anomaly in the sliding member of the machine installation orthe member relevant to the sliding member of the machine installation,thereby diagnosing the presence or absence of an anomaly in the slidingmember of the machine installation or the member relevant to the slidingmember of the machine installation.

In the described anomaly diagnosis method of the machine installation,to conduct frequency analysis or envelope analysis on the signalprovided by detecting sound or vibration at the bearing use point andextract frequency peaks from the actual measurement frequency spectrumdata provided accordingly, for the actual measurement frequency spectrumdata, the level difference between each arbitrary data point and itsimmediately preceding data point and a gradient are calculated to find apeak value, so that extracting of a valley can be circumvented andhigher-accuracy diagnosis is made possible as compared with the case ofextracting from frequency components of large spectrum levels.

To accomplish the object, according to the invention, there is provided

(9) an anomaly diagnosis method of a machine installation for detectingsound or vibration produced from a sliding member of the machineinstallation or a member relevant to the sliding member of the machineinstallation, analyzing a detection signal, and diagnosing the presenceor absence of an anomaly caused by the sliding member of the machineinstallation or the member relevant to the sliding member of the machineinstallation, characterized by:

converting an analog signal of sound or vibration produced from thesliding member of the machine installation or the member relevant to thesliding member of the machine installation into digital form to generateactual measurement digital data, selecting any desired time domain forthe generated actual measurement digital data, performing appropriateanalysis processing of frequency analysis, envelope analysis, etc., fordata in the selected time domain to generate actual measurementfrequency spectrum data, calculating the level difference between eacharbitrary data point and its immediately preceding data point and agradient to find a peak value for the generated actual measurementfrequency spectrum data, and comparing a peak value on the actualmeasurement frequency spectrum data for the frequency component causedby anomaly in the sliding member of the machine installation or themember relevant to the sliding member of the machine installation,thereby diagnosing the presence or absence of an anomaly in the slidingmember of the machine installation or the member relevant to the slidingmember of the machine installation.

To accomplish the object, according to the invention, there is provided

(10) an anomaly diagnosis method of a machine installation for detectingsound or vibration produced from a sliding member of the machineinstallation or a member relevant to the sliding member of the machineinstallation, analyzing a detection signal, and diagnosing the presenceor absence of an anomaly caused by the sliding member of the machineinstallation or the member relevant to the sliding member of the machineinstallation, characterized by:

converting an analog signal of sound or vibration produced from thesliding member of the machine installation or the member relevant to thesliding member of the machine installation into digital form to generateactual measurement digital data, selecting any desired time domain forthe generated actual measurement digital data, performing appropriateanalysis processing of frequency analysis, envelope analysis, etc., fordata in the selected time domain to generate actual measurementfrequency spectrum data, selecting any desired frequency domain for thegenerated actual measurement spectrum data, filtering assuming that theselected frequency domain is a filter band to generate new actualmeasurement frequency spectrum data, calculating the level differencebetween each arbitrary data point and its immediately preceding datapoint and a gradient to find a peak value for the generated actualmeasurement frequency spectrum data, and comparing a peak value on theactual measurement frequency spectrum data for the frequency componentcaused by anomaly in the sliding member of the machine installation orthe member relevant to the sliding member of the machine installation,thereby diagnosing the presence or absence of an anomaly in the slidingmember of the machine installation or the member relevant to the slidingmember of the machine installation.

Thus, in the anomaly diagnosis method of the machine installationdescribed above in (9) or (10), the user can use the pointing device 215such as a mouse to select the analysis range and the filtering range forany desired waveform range in the time waveform after AD conversion andthe spectrum waveform after frequency analysis, so that a signal havinga high S/N ratio can be extracted by simple operation andhigher-accuracy diagnosis is made possible.

The anomaly diagnosis method of the machine installation according tothe invention is characterized by the fact that

(11) in the anomaly diagnosis method described above in any of (6) to(10), the frequency component caused by anomaly in the sliding member ofthe machine installation or the member relevant to the sliding member ofthe machine installation corresponds to an abnormal part of the machineinstallation or a machine.

The anomaly diagnosis method of the machine installation according tothe invention is characterized by the fact that

(12) in the anomaly diagnosis method described above in any of (6) to(10), the frequency component caused by anomaly in the sliding member ofthe machine installation or the member relevant to the sliding member ofthe machine installation is a frequency component caused by an anomalyof a bearing used with the machine installation.

In the anomaly diagnosis method according to the invention, the peakvalue used as the determination criterion useful for decreasing thecalculation load at the diagnosis processing time and improving thereliability of the diagnosis may be found by first detecting sound orvibration produced from the sliding member of the machine installationor the member relevant to the sliding member of the machine installationand next converting the analog signal of the detected sound or vibrationinto digital form to generate actual measurement digital data and thencalculating the level difference between each arbitrary data point andits immediately preceding data point and a gradient for the generatedactual measurement frequency spectrum data without finding the peakvalue for the waveform already subjected to frequency analysis orenvelope analysis as described above in (8) to (10).

To accomplish the object, according to the invention, there is provided

(13) an anomaly diagnosis method of a machine installation fordiagnosing the presence or absence of an anomaly in a sliding member,etc., of the machine installation by analyzing sound or vibrationproduced by the machine installation containing the sliding member,characterized by:

detecting a signal representing sound or vibration produced by thesliding member, etc., of the machine installation, generating actualmeasurement frequency spectrum data of a frequency spectrum of thedetected signal or an envelope signal thereof, executing a basicfrequency component comparison process of checking whether or not thefrequency at an appearance point of a peak equal to or higher than areference level on the actual measurement frequency spectrum datamatches the basic frequency at which a peak appears because of ananomaly in a specific part of the sliding member, etc., and when thefrequency at the appearance point of the peak equal to or higher thanthe reference level on the actual measurement frequency spectrum datamatches the basic frequency in the basic frequency component comparisonprocess, executing a low-frequency component comparison process ofchecking the presence or absence of a frequency component having a peakequal to or higher than the reference level in a low-frequency rangeequal to or less than the basic frequency on the actual measurementfrequency spectrum data;

when the actual measurement frequency spectrum data does not have a peakequal to or higher than the reference level in the low-frequency rangeequal to or less than the basic frequency in the low-frequency componentcomparison process, diagnosing the sliding member, etc., as an anomalyin the specific part;

when the actual measurement frequency spectrum data has a peak equal toor higher than the reference level in the low-frequency range equal toor less than the basic frequency in the low-frequency componentcomparison process, further executing a harmonic component comparisonprocess of determining whether or not the harmonic of the frequencycomponent having the peak equal to or higher than the reference level inthe low-frequency range equal to or less than the basic frequencymatches the basic frequency; and

when the harmonic of the frequency component having the peak equal to orhigher than the reference level in the low-frequency range equal to orless than the basic frequency does not match the basic frequency in theharmonic component comparison process, diagnosing the sliding member,etc., as an anomaly in the specific part; when the harmonic matches thebasic frequency, diagnosing the sliding member, etc., as no anomaly inthe specific part.

The expression “sliding member, etc., of machine installation” mentionedabove is used to mean that the sliding member of the machineinstallation and a sliding member relevant member joined to the slidingmember or supporting the sliding member in the machine installation areincluded.

For example, bearings, ball screws, linear guides, motors, etc., comeunder the sliding members.

In the described anomaly diagnosis method of the machine installation,the basic frequency component comparison process of checking whether ornot the frequency at an appearance point of a peak equal to or higherthan a reference level on the actual measurement frequency spectrum datamatches the basic frequency at which a peak appears because of ananomaly in a specific part of the sliding member, etc., is executed. Ifthe frequency at the appearance point of the peak equal to or higherthan the reference level on the actual measurement frequency spectrumdata matches the basic frequency in the basic frequency componentcomparison process, subsequently the low-frequency component comparisonprocess and the harmonic component comparison process are executedwithout immediately diagnosing the sliding member, etc., as an anomaly.

If the low-frequency component comparison process and the harmoniccomponent comparison process are executed, whether or not the peak equalto or higher than the reference level in the basic frequency on theactual measurement frequency spectrum data is caused by any other factorof overlap of frequency components of rotation components, etc., of thesliding member, etc., the effect of harmonic, etc., for example, ratherthan an anomaly of damage, etc., in the sliding member.

Thus, when the frequency at the appearance point of the peak equal to orhigher than the reference level on the actual measurement frequencyspectrum data matches the basic frequency in the basic frequencycomponent comparison process, further the low-frequency componentcomparison process and the harmonic component comparison process areexecuted, whereby erroneous diagnosis of assuming that the peak causedby any other factor of overlap of frequency components of rotationcomponents, etc., of the sliding member, etc., the effect of harmonic,etc., is caused by an anomaly in the sliding member, etc., can becircumvented and the reliability of diagnosing the presence or absenceof an anomaly in the sliding member, etc., can be improved.

To accomplish the object, according to the invention, there is provided

(14) an anomaly diagnosis method of a machine installation for detectingsound or vibration produced from a sliding member of the machineinstallation, analyzing a detected vibration signal, and diagnosing thepresence or absence of an anomaly caused by the sliding member,characterized by:

converting an analog signal of sound or vibration produced from thesliding member into a digital signal to generate actual measurementdigital data, performing appropriate analysis processing of frequencyanalysis, envelope analysis, and the like for the actual measurementdigital data to generate actual measurement frequency spectrum data, anddiagnosing the presence or absence of an anomaly in a specific part ofthe sliding member of the machine installation based on the presence orabsence of a peak on the actual measurement frequency spectrum data forfirst-order, second-order, fourth-order value of frequency componentoccurring when the specific part of the sliding member is abnormal.

To accomplish the object, according to the invention, there is provided

(15) an anomaly diagnosis apparatus of a machine installation fordetecting sound or vibration produced from a sliding member of themachine installation, analyzing a detected vibration signal, anddiagnosing the presence or absence of an anomaly caused by the slidingmember of the machine installation, the anomaly diagnosis apparatusincluding:

AD conversion means for converting an analog signal of sound orvibration produced from the sliding member of the machine installationinto a digital signal to generate actual measurement digital data, andcomputation processing means for performing appropriate analysisprocessing of frequency analysis, envelope analysis, and the like forthe actual measurement digital data to generate actual measurementfrequency spectrum data, and diagnosing the presence or absence of ananomaly in a specific part of the sliding member based on the presenceor absence of a peak on the actual measurement frequency spectrum datafor first-order, second-order, fourth-order value of frequency componentoccurring when the specific part of the sliding member is abnormal.

According to the anomaly diagnosis method and apparatus of the machineinstallation described above in (14) and (15), the comparison process ofchecking the presence or absence of a peak on the actual measurementfrequency spectrum data corresponding to frequency components occurringwhen each specific part of the sliding member of the machineinstallation is abnormal is limited to three times of the first-order,second-order, and fourth-order values of the frequency componentsoccurring when the specific part of the sliding member of the machineinstallation is abnormal and therefore the computation processing amountin the comparison process is drastically decreased and the load on thecomputation processing means is drastically lightened as compared withthe related art case where the comparison process is repeated for all ofa large number of frequency components of first-order to high-orderfrequency components, for example.

Further, since the comparison process is executed three times of thefirst-order, second-order, and fourth-order values of the frequencycomponents occurring when the specific part of the sliding member of themachine installation is abnormal, erroneous diagnosis caused by theeffect of noise, etc., is hard to occur and highly reliable diagnosis ismade possible as compared with the case where a determination is madebased only on the first-order component of frequency componentsoccurring under abnormal condition.

The anomaly diagnosis method of the machine installation according tothe invention is characterized by the fact that

(16) in the anomaly diagnosis method and apparatus of the machineinstallation described above in (14), after generating the actualmeasurement frequency spectrum data, an effective value of the actualmeasurement frequency spectrum data is calculated, a threshold value isset based on the effective value, and the peak on the actual measurementfrequency spectrum data for the first-order, second-order, fourth-ordervalue of the frequency component occurring when the specific part of thesliding member 403 is abnormal is handled as the effective peak onlywhen the peak exceeds the threshold value.

The anomaly diagnosis apparatus of the machine installation according tothe invention is characterized by the fact that

(17) in the anomaly diagnosis apparatus of the machine installationdescribed above in (15), after generating the actual measurementfrequency spectrum data, the computation processing means 413 calculatesan effective value of the actual measurement frequency spectrum data,sets a threshold value based on the effective value, and handles thepeak on the actual measurement frequency spectrum data for thefirst-order, second-order, fourth-order value of the frequency componentoccurring when the specific part of the sliding member 403 is abnormalas the effective peak only when the peak exceeds the threshold value.

In doing so, for example, before a computation process for comparisonfor the peaks on the actual measurement frequency spectrum datacorresponding to the first-order value, the second-order value, and thefourth-order value of the frequency components occurring when thespecific part of the sliding member of the machine installation isabnormal is executed, waste of executing the comparison process forinsignificant peaks can be avoided.

To accomplish the object, according to the invention, there is provided

(18) an anomaly diagnosis method of a machine installation for detectingsound or vibration produced from a sliding member of the machineinstallation, analyzing a detected vibration signal, and diagnosing thepresence or absence of an anomaly caused by the sliding member,characterized by:

converting an analog signal of sound or vibration produced from thesliding member of the machine installation into a digital signal togenerate actual measurement digital data, performing appropriateanalysis processing of frequency analysis, envelope analysis, and thelike for the actual measurement digital data to generate actualmeasurement frequency spectrum data, and then calculating an effectivevalue or an average value of the actual measurement frequency spectrumdata, setting the calculated effective value or average value as areference level, and estimating the magnitude of damage to a specificpart of the sliding member of the machine installation causing ananomaly to occur from the level difference between level on the actualmeasurement frequency spectrum data corresponding to the first-ordervalue of the frequency component occurring when the specific part of thesliding member of the machine installation is abnormal and the referencelevel.

Generally, growing of the peak level on the actual measurement frequencyspectrum caused by damage to the sliding member of the machineinstallation becomes most noticeable at the peak corresponding to thefirst-order value of the frequency components caused by the anomaly.

Thus, as shown in the anomaly diagnosis method of the machineinstallation described above in (18), the level difference between thelevel on the actual measurement frequency spectrum data corresponding tothe first-order value of the frequency components occurring when thespecific part of the sliding member of the machine installation isabnormal and the effective value of the actual measurement frequencyspectrum data is calculated, whereby the magnitude of the damage can beestimated efficiently by performing minimum computation processing, andit is made possible to determine the appropriate replacement time of thedamaged part.

To accomplish the object, according to the invention, there is provided

(19) an anomaly diagnosis apparatus of a machine installation fordetecting sound or vibration produced from the machine installationcontaining a sliding member, analyzing a detected vibration signal, anddiagnosing the presence or absence of an anomaly caused by the machineinstallation containing the sliding member, the anomaly diagnosisapparatus including:

vibration detection means for detecting sound or vibration produced bythe machine installation containing the sliding member and outputting anelectric signal responsive to the detected sound or vibration;

sampling reference setting means for setting a reference value toexclude an area where the effect of noise is large from the outputsignal of the vibration detection mean;

sampling means for extracting effective actual measurement data with anarea where the effect of noise is large excluded from the output signalof the vibration detection means based on the reference value set in thesampling reference setting means; and

computation processing means for performing appropriate analysisprocessing of envelope analysis, etc., for the effective actualmeasurement data extracted by the sampling means to generate actualmeasurement frequency spectrum data and diagnosing the presence orabsence of an anomaly in a specific part of the machine installationcontaining the sliding member based on the presence or absence of a peakon the actual measurement frequency spectrum data for frequencycomponent occurring when the specific part of the machine installationcontaining the sliding member is abnormal.

According to the anomaly diagnosis apparatus of the machine installationdescribed above in (19), the sampling means automatically executesremoval of the noise component from the actual measurement data detectedby the vibration detection means from the machine installationcontaining the sliding member based on the reference value set in thesampling reference setting means.

Therefore, the person in charge of diagnosis for managing the anomalydiagnosis apparatus of the machine installation need not check theactual measurement data to remove the noise component each time, andinterrupting of processing of the anomaly diagnosis apparatus of themachine installation to check the actual measurement data does notoccur.

The noise component is removed uniformly by machine processing based onthe reference value, so that the skill degree of the person in charge ofdiagnosis does not affect the noise component removal rate.

Further, since the person in charge of diagnosis need not check theactual measurement data to remove the noise component, an output unitfor displaying the actual measurement data detected by the vibrationdetection means in such a manner that the person in charge of diagnosiscan check the actual measurement data can also be omitted.

Preferably, in the anomaly diagnosis apparatus of the machineinstallation according to the invention, the sampling reference settingmeans may automatically calculate the reference value to select an areawhere an excessive value caused by the effect of noise is not containedfrom the average level of the electric signals detected by the vibrationdetection means, the operation timing of the vibration detection means,etc., and a predetermined constant, etc.

If the sampling reference setting means thus automatically sets thereference value, the data entry operation of the person in charge ofdiagnosis in the anomaly diagnosis apparatus of the machine installationto set the reference value is decreased and the load on the person incharge of diagnosis is lightened and at the same time, the required timefor the data entry operation can be saved.

The machine installation containing the sliding member contains one ormore sliding members and means a machine installation or a machine wherein vibration occurs as the sliding members slide, and also contains ballscrews, linear guides, motors, etc., in addition to rolling bearings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of a first embodiment of an anomalydiagnosis apparatus of a machine installation according to theinvention.

FIG. 2 is a flowchart to show the operation of the first embodiment ofthe invention.

FIG. 3 is a schematic representation of the format of one input screenwhen a diagnosis request is sent to a diagnosis processing server.

FIG. 4 is a schematic representation of the format of another inputscreen when a diagnosis request is sent to a diagnosis processing serverin the first embodiment of the invention.

FIG. 5 is a schematic representation listing occurrence frequenciescaused by an anomaly of a specific part of a bearing when a slidingmember is a bearing.

FIG. 6 is a drawing to show the format of a diagnosis result screendisplayed on a user information processing terminal in the firstembodiment of the invention.

FIG. 7 is a schematic representation of a detailed format example of thecontents of diagnosis result displayed on the user informationprocessing terminal in the first embodiment of the invention.

FIG. 8 is a block diagram to show the configuration of a secondembodiment of an anomaly diagnosis apparatus of a machine installationaccording to the invention.

FIG. 9 is a block diagram to show the configuration of a thirdembodiment of an anomaly diagnosis apparatus of a machine installationaccording to the invention.

FIG. 10 is a block diagram to show the configuration of a fourthembodiment of an anomaly diagnosis apparatus of a machine installationaccording to the invention.

FIG. 11 is a block diagram to show the configuration of a fifthembodiment of an anomaly diagnosis apparatus of a machine installationaccording to the invention.

FIG. 12 is a flowchart to show a procedure of anomaly diagnosisprocessing executed by the apparatus in FIG. 11.

FIG. 13 is a drawing to show the relationship between damage points in arolling bearing and frequencies.

FIG. 14 provides a waveform provided by performing envelope processing.

FIG. 15 provides another waveform provided by performing envelopeprocessing.

FIG. 16 provides another waveform provided by performing envelopeprocessing.

FIG. 17 is a drawing to describe a peak value calculation method of theinvention.

FIG. 18 is a drawing to describe a peak value extracting method fromfrequency components of large spectrum levels in order.

FIG. 19 is a drawing to describe peak value extraction by the peak valuecalculation method of the invention.

FIG. 20 is a flowchart of diagnosis processing according to a sixembodiment of an anomaly diagnosis apparatus of a machine installationaccording to the invention.

FIG. 21 is a schematic block diagram of a seventh embodiment of ananomaly diagnosis apparatus of a machine installation according to theinvention.

FIG. 22 is a drawing to describe diagnosis processing of the anomalydiagnosis apparatus of a machine installation shown in FIG. 21 and is araw waveform chart of signal data.

FIG. 23 is a drawing to describe diagnosis processing of the anomalydiagnosis apparatus of a machine installation shown in FIG. 21 and is anenlarged view of selection range part of the raw waveform of signaldata.

FIG. 24 is a drawing to describe diagnosis processing of the anomalydiagnosis apparatus of a machine installation shown in FIG. 21 and is afrequency spectrum of signal data.

FIG. 25 is a drawing to describe diagnosis processing of the anomalydiagnosis apparatus of a machine installation shown in FIG. 21 and is adrawing to show an envelope frequency spectrum of signal data.

FIG. 26 is a drawing to describe diagnosis processing of the anomalydiagnosis apparatus of a machine installation shown in FIG. 21 and is adrawing to show frequency spectrum of signal data and selected range.

FIG. 27 is a drawing to describe diagnosis processing of the anomalydiagnosis apparatus of a machine installation shown in FIG. 21 and is adrawing to show frequency spectrum after being filtered.

FIG. 28 is a drawing to describe diagnosis processing of the anomalydiagnosis apparatus of a machine installation shown in FIG. 21 and is adrawing to show envelope frequency spectrum after being filtered.

FIG. 29 is a flowchart to show a diagnosis processing procedure of ananomaly diagnosis method executed by an eighth embodiment of an anomalydiagnosis apparatus according to the invention.

FIG. 30 is a schematic representation of a waveform when the diagnosisprocessing in FIG. 29 is executed for a damaged inner ring of a rollingbearing as the sliding member according to the invention and a peakequal to or higher than the reference level does not appear inlow-frequency range in comparison process with the basic frequency ofpeak caused by inner ring damage.

FIG. 31 is a schematic representation of a waveform when the diagnosisprocessing in FIG. 29 is executed for an undamaged outer ring of arolling bearing as the sliding member according to the invention andharmonic of low frequency and basic frequency match in comparisonprocess with the basic frequency of peak caused by outer ring damage.

FIG. 32 is a schematic representation of a waveform when the diagnosisprocessing in FIG. 29 is executed for a damaged inner ring of a rollingbearing as the sliding member according to the invention and harmonic oflow frequency and basic frequency match in comparison process with thebasic frequency of peak caused by inner ring damage.

FIG. 33 is a block diagram to show the configuration of a ninthembodiment of an anomaly diagnosis apparatus of a machine installationfor executing an anomaly diagnosis method of a machine installationaccording to the invention.

FIG. 34 is a flowchart to show a processing procedure of the anomalydiagnosis apparatus of a machine installation shown in FIG. 33.

FIG. 35 is a waveform chart to show an actual measurement frequencyspectrum when abnormal vibration occurs due to a flaw of an outer ringof a rolling bearing as a sliding member of the machine installation.

FIG. 36 is a flowchart to show a detailed processing procedure ofcomparison process between frequency component when an anomaly occursand a peak part of actual measurement frequency spectrum data in theninth embodiment of the invention.

FIG. 37 is a waveform chart to show comparison parts with frequencycomponents under abnormal condition on the actual measurement frequencyspectrum when abnormal vibration occurs due to a flaw of the outer ringof the rolling bearing as the sliding member of the machineinstallation.

FIG. 38 is a waveform chart to show comparison parts with frequencycomponents under abnormal condition on the actual measurement frequencyspectrum when abnormal vibration occurs due to a flaw of a rollingelement of the rolling bearing as the sliding member of the machineinstallation.

FIG. 39 is a waveform chart of an actual measurement frequency spectrumshowing frequency components and reference level to be compared toestimate the magnitude of peel according to the anomaly diagnosis methodof the machine installation according to the invention.

FIG. 40 is a correlation drawing between the magnitude of peel ofrolling element surface causing abnormal vibration and the leveldifference between each peak appearing on the actual measurementfrequency spectrum and the reference level in a rolling bearingdiagnosed as the sliding member of the machine installation.

FIG. 41 is a schematic block diagram of a tenth embodiment of an anomalydiagnosis apparatus of a machine installation according to theinvention.

FIG. 42 is a flowchart to show a processing procedure of the anomalydiagnosis apparatus of a machine installation shown in FIG. 41.

FIG. 43 is a schematic representation of a setting example of areference value to exclude an area where the effect of noise is largefrom a signal detected by vibration detection means of the anomalydiagnosis apparatus of a machine installation shown in FIG. 41.

FIG. 44 is a waveform chart of an actual measurement frequency spectrumgenerated by computation processing means in the anomaly diagnosisapparatus of a machine installation shown in FIG. 41.

FIG. 45 is a waveform chart of actual measurement frequency spectrumdata generated as a signal provided by AD conversion means is subjectedto processing of envelop analysis, etc., without removing noise bysampling means in the anomaly diagnosis apparatus of a machineinstallation shown in FIG. 41.

FIG. 46 is a block diagram to show the configuration of an anomalydiagnosis apparatus of a machine installation in a related art.

In the drawings, numeral 1 denotes diagnosis processing server, numeral2 denotes network, numeral 3 denotes user information processingterminal, numerals 11 and 31 denote communication sections, numeral 12denote diagnosis section, numeral 13 denotes user database, numeral 14denotes sound/vibration database, numeral 15 denotes specificationdatabase, numeral 16 denotes measure database, numeral 17 denotestemperature database, numeral 32 denotes input section, numeral 33denotes display section, numeral 121 denotes anomaly diagnosisapparatus, numeral 123 denotes network, numeral 125 denotes diagnosisprocessing server, numeral 127 denotes machine installation containingsliding member, numeral 129 denotes information processing terminal,numeral 131 denotes actual measurement vibration data, numeral 141denotes actual measurement analysis program, numeral 143 denotessound/vibration database, numeral 143 a denotes determination criteriondata, numeral 145 denotes determination program, numeral 148 denotesdiagnosis processing menu, numeral 153 denotes input means, numeral 154denotes display means, numeral 181 denotes customer informationdatabase, numeral 191 denotes measure database, numeral 203 denotesmachine installation containing sliding member, numeral 211 denotessensor, numeral 212 denotes amplifier, numeral 213 denotes AD converter,numeral 214 denotes diagnosis computer, numeral 215 denotes pointingdevice, numeral 216 denotes AD converter, numeral 217 denotes graphicaluser interface, numeral 401 denotes anomaly diagnosis apparatus ofmachine installation, numeral 403 denotes machine installationcontaining sliding member, numeral 405 denotes vibration detectionmeans, numeral 407 denotes amplification means, numeral 409 denotes ADconversion means, numeral 413 denotes computation processing means,numeral 501 denotes anomaly diagnosis apparatus of machine installation,numeral 503 denotes machine installation containing sliding member,numeral 505 denotes vibration detection means, numeral 507 denotesamplification means, numeral 509 denotes AD conversion means, numeral511 denotes sampling reference setting means, numeral 513 denotes inputmeans, numeral 515 denotes sampling means, and numeral 517 denotescomputation processing means.

BEST MODE FOR CARRYING OUT THE INVENTION

Embodiments of an anomaly diagnosis apparatus and method of a machineinstallation according to the invention will be discussed in detail withreference to the accompanying drawings.

FIG. 1 is a schematic block diagram of a first embodiment of an anomalydiagnosis apparatus of a machine installation according to theinvention.

The anomaly diagnosis apparatus of a machine installation of the firstembodiment includes a diagnosis processing server 1 and a userinformation processing terminal 3 which are connected through a network2 such as the Internet. For example, general-purpose personal computersthat can be connected to the network 2 through a predeterminedcommunication interface can be used as the diagnosis processing server 1and the user information processing terminal 3.

The diagnosis processing server 1 includes a communication section 11, adiagnosis section 12, a user database 13, a sound/vibration database 14,a specification database 15, a measure database 16, and a temperaturedatabase 17. In the drawing, the database is abbreviated as DB.

The user information processing terminal 3 can be connected to thenetwork 2 when necessary, and includes a communication section 31, aninput section 32, and a display section 33. FIG. 1 shows only one userinformation processing terminal 3, but a plurality of user informationprocessing terminals 3 can be connected.

The input section 32 inputs sound or vibration data occurring from oneor more sliding member use points contained in the machine installationand information for identifying the sliding member (containing partnumber and sliding member use condition information). The communicationsections 31 and 11 transfer data to and from the network. The diagnosissection 12 diagnoses an anomaly in the machine installation based on thesound or vibration data occurring from one or more sliding member usepoints of the machine installation and the information for identifyingthe sliding member, input through the network.

Generally, various machine elements containing a part sliding inrotational motion or linear motion, such as bearings, ball screws,linear guides, and motors, come under the sliding members in the machineinstallation.

An anomaly diagnosis based on the sound or vibration data is madespecifically by a method of computing frequency analysis of the originalwaveform of the vibration data, frequency analysis after envelopeprocessing, peak factor, etc., and comparing them with reference datacomputed based on the specifications and use conditions of the slidingmember or initially registered data, etc. Various methods are well knownand can be used.

The user database 13 stores for each user the ID and name of the useraccessing the diagnosis processing server 1 through the user informationprocessing terminal 3, the sliding member used by the user, informationrequired for identifying the sliding member, the use conditions of thesliding member, the past diagnosis results, the measures, etc.

The sound/vibration database 14 stores sound or vibration datatransmitted from the user information processing terminal 3.

The specification database 15 stores the specifications of the slidingmembers used with the machine installation.

The measure database 16 stores advice responsive to the diagnosis resultof the machine installation.

Next, the operation of the anomaly diagnosis apparatus of the embodimentwill be discussed based on a flow in FIG. 2.

It is assumed that the sliding member used with the machine installationis a bearing and that sound data is used. The user who wants to make ananomaly diagnosis collects sound data of the bearing use point with amicrophone, etc., and converts the collected analog data into digitalform to create a file in a predetermined format, for example, a WAV file(step 101). The sound data may be acquired at the user informationprocessing terminal 3 or using any other dedicated apparatus. Thecollected analog data may be converted directly into digital form or thedata once recorded on magnetic tape, etc., may be converted into digitalform.

Next, the user accesses the diagnosis processing server 1 through thenetwork 2 and logs in to the diagnosis processing server 1 (step 102).The diagnosis processing server 1 may be accessed freely or may beaccessible only by limited members. To access the diagnosis processingserver 1 freely, preferably the user is once registered and the user IDis issued and at the second time or later, the user accesses thediagnosis processing server 1 using the user ID. The example assumesthat the user is already registered.

The diagnosis processing server 1 checks the user ID (step 103). If theuser is already registered, the diagnosis processing server 1 causes theuser information processing terminal 3 to display a data input screenon, prompting the user to enter data (step 104). FIG. 3 shows a displayscreen example in this case.

The user enters the bearing part number and the use conditions of thenumber of revolutions, used lubricant, etc., and transmits the alreadyacquired sound data file (step 105).

As the sound data file, a file previously stored in a storage section(not shown) in the user information processing terminal 3 may be used ora file read from an FD (floppy disk), etc., may be used. If an anomalydiagnosis of the same bearing was made in the past (for example, in aroutine check, etc.), the user needs only to enter the diagnosis numberand may omit the bearing part number and the use conditions. FIG. 4shows a display screen example in the case.

In this case, the user enters the diagnosis number in an area 41 and thesound data file name in an area 42.

Upon reception of the bearing part number, the use conditions, and thesound data file, the diagnosis processing server 1 acquires the bearingspecifications from the specification database 15 based on the bearingpart number (step 106) and calculates the vibration occurrence frequencywhen the bearing is used based on the acquired specifications and useconditions (step 107). The diagnosis processing server 1 also performsanalysis processing of the data in the sound data file (step 108).

As the analysis processing, filtering, envelope, FFT processing, etc.,is performed as required. The diagnosis processing server 1 makes acomparison between the analysis result at step 108 and the calculationresult at step 107 (step 109) and determines whether or not an anomalyexists (step 110). The comparing and determining are executed bycomparing the occurrence frequencies caused by the bearing as shown inFIG. 5, for example.

Subsequently, the diagnosis processing server 1 retrieves the measuredatabase 16 based on the determination result and extracts the cause andmeasures (step 111). Then, it transmits the diagnosis result to the userinformation processing terminal 3 and causes the user informationprocessing terminal 3 to display the diagnosis result (step 112). FIG. 6shows a display screen example of the diagnosis result. The contentsshown in FIG. 7 are displayed in an area 51. In FIG. 7, the diagnosisconditions are displayed in an area 511 and the diagnosis result of thetype of produced sound, the occurrence source, evaluation, comments,etc., is displayed in an area 512. Referring again to FIG. 6, the causeof the anomaly and the measures against the anomaly are displayed in anarea 52. The reliability of the determination, the remaining life(guideline for the time until the sliding member does not function as abearing), and the like are also displayed for advising the user of thereplacement time. The diagnosis processing server 1 registers in thesound/vibration database 14 (step 113).

Upon acquisition of the diagnosis result, the user logs off (step 114).To continue diagnosis, the user may again enter different data at step104.

In the described anomaly diagnosis apparatus of the machineinstallation, when the user of the machine installation wants diagnosisof the presence or absence of an anomaly in the bearing of the slidingmember used with the machine installation, if the user transmits thesound or vibration data at the use point of the bearing on the machineinstallation required for the anomaly diagnosis, the informationidentifying the used bearing (bearing part number, etc.), the bearinguse condition information, etc., from the user information processingterminal 3 through the network 2 to the diagnosis processing server 1,the diagnosis processing server 1 automatically executes anomalydiagnosis processing based on the received data and further transmitsthe diagnosis result through the network 2 to the user informationprocessing terminal 3.

To execute periodic anomaly diagnosis, for example, for one or morebearings on the machine installation, if the use conditions of eachbearing and the information for identifying each bearing transmitted bythe user to the diagnosis processing server 1 are once prepared andstored in a storage unit, etc., of the user information processingterminal 3, unless the information is changed, it can be usedrepeatedly, similar information need not be prepared from the beginningeach time an anomaly diagnosis request is made, and the burden requiredfor preparing information required for making an anomaly diagnosisrequest can be lightened drastically.

Further, an anomaly diagnosis request is sent directly to the diagnosisprocessing server 1 that can automatically execute anomaly diagnosisprocessing not via a window job for the person in charge to accept therequest manually, and the requested diagnosis processing is executedpromptly within the scope of the information processing performance ofthe diagnosis processing server 1, so that the user can get thediagnosis result early.

Therefore, if the user does not have a dedicated analytical instrumentor a skill required for anomaly diagnosis of the sliding member, theuser can make an anomaly diagnosis request easily with a small burdenand moreover can get the diagnosis result promptly and deal withoccurrence of the anomaly rapidly.

FIG. 8 is a block diagram to show the configuration of a secondembodiment of an anomaly diagnosis apparatus of a machine installationaccording to the invention.

An anomaly diagnosis apparatus 121 of a machine installation of thesecond embodiment is made up of a diagnosis processing server 125 of aninformation processing apparatus (computer) connected to a network 123and an information processing terminal 129 which is installed in theuser using a machine installation 127 containing a sliding memberwherein the presence or absence of an anomaly is to be diagnosed and canaccess the diagnosis processing server 125 through the network 123, andthe Internet is used as the network 123.

In the embodiment, the sliding member used with the machine installation127 is a rolling bearing.

Uploaded in an executable data format in the information processingterminal 129 to the diagnosis processing server 125 are an actualmeasurement data analysis program 141 for analyzing actual measurementvibration data 131 recording sound or vibration occurring when themachine installation 127 containing the sliding member operates,determination criterion data 143 a recording information used as thedetermination criterion of the presence or absence of an anomaly at aspecific part of the machine installation 127 containing the slidingmember, and a determination program 145 for comparing the analysisresult of the actual measurement data analysis program 141 with thedetermination criterion data 143 a and diagnosing the presence orabsence of an anomaly for the specific part of the machine installation127 containing the sliding member.

As the determination criterion data 143 a, the frequency component ofvibration occurring when the specific part of the machine installationis abnormal and the standard peak level in the frequency component arecalculated from the specifications, etc., of the machine installationand are set; the determination criterion data 143 a is managed in asound/vibration database 143 constructed in a data storage section 147of the diagnosis processing server 125.

Various databases useful for diagnosis processing, such as aspecification database 161 storing information of the specifications,etc., of the machine installation, a customer information database 181storing information concerning the user using the machine installationto be diagnosed, and a measure database 191 storing information ofmeasures, etc., returned to the user in response to the abnormalcondition in addition to the above-mentioned sound/vibration database143, are constructed in the data storage section 147.

The actual measurement data analysis program 141 and the determinationprogram 145 are uploaded to a diagnosis program storage section 146 ofthe diagnosis processing server 125.

In the embodiment, when the actual measurement data analysis program 141is executed in the information processing terminal 129, it performsappropriate analysis processing of envelope analysis, etc., for theactual measurement vibration data 131 and generates actual measurementfrequency spectrum data indicating sound or vibration occurring when thesliding member used with the machine installation 127 rotates.

When the determination program 145 is executed in the informationprocessing terminal 129, it adopts as the data stored in thedetermination criterion data 143 a the frequency component occurringwhen the specific part of the machine installation 127 is abnormal asthe determination criterion, and determines the presence or absence ofan anomaly in the machine installation 127 containing the sliding memberand locates the anomaly occurrence point based on whether or not a peakvalue of a given level or more appears at the determination criterionposition on the actual measurement frequency spectrum data of theanalysis result of the actual measurement data analysis program 141. Thedetermination program 145 outputs the determination result to displaymeans information processing terminal 54 or if the user makes a request,outputs the determination result to a printer connected to theinformation processing terminal 129.

The actual measurement data analysis program 141, the determinationprogram 145, and the determination criterion data 143 a uploaded to thediagnosis processing server 125 are managed by a diagnosis processingmenu 148 built in the diagnosis processing server 125 so that they canbe downloaded according to a given access procedure.

In the anomaly diagnosis apparatus 121 of the embodiment, the measuresto be taken by the user or the manufacturer in response to the abnormalcondition of the machine installation 127 containing the sliding memberdiagnosed are set in detail in the measure database 191 of the diagnosisprocessing server 125. When the user downloads the actual measurementdata analysis program 141 and the determination program 145, the measuredatabase 191 is also downloaded together and the determination program145 references the measure database 191 in response to the determinationresult and outputs the measures to be taken to eliminate the anomaly tothe display means 154, etc.

The information processing terminal 129 is a personal computer and isconnected to the network 123 through a communication interface 151.

Connected to the information processing terminal 129 are input means 153of a keyboard, etc., and the display means 154 implemented as a CRT,etc., capable of displaying the various contents of the informationprocessing terminal 129, such as the data stored in the informationprocessing terminal 129 and the processing state in the informationprocessing terminal 129.

A vibration measurement apparatus 156 for detecting sound or vibrationof the rotation operation of the sliding member used with the machineinstallation 127 is connected to the information processing terminal 129through an actual measurement vibration data input interface 157. Theinformation processing terminal 129 inputs the actual measurementvibration data 131 provided by the vibration measurement apparatus 156through the actual measurement vibration data input interface 157 andstores the data in a predetermined data storage section.

The user accesses the diagnosis processing server 125 through thenetwork 123 from the information processing terminal 129 and downloadsthe actual measurement data analysis program 141, the determinationprogram 145, and the determination criterion data 143 a into theinformation processing terminal 129 of the user and on the other hand,inputs the actual measurement vibration data 131 to the informationprocessing terminal 129 of the user through the actual measurementvibration data input interface 157 and executes the actual measurementdata analysis program 141 and the determination program 145 in theinformation processing terminal 129 of the user for diagnosing thepresence or absence of an anomaly in the machine installation 127containing the sliding member in the information processing terminal 129of the user.

An authentication program for comparing with customer information ofinformation required for authenticating the user using the machineinstallation 127 containing the sliding member and allowing the user todownload the actual measurement data analysis program 141, thedetermination program 145, and the determination criterion data 143 awhen authenticating the user as the authorized user may be built in thediagnosis processing menu 148 of the diagnosis processing server 125 andonly when the user is authenticated as the authorized user in anauthentication check process 149 of the authentication program, as shownin FIG. 9, the user may be allowed to download the actual measurementdata analysis program 141 and the determination program 145 as the useroperates the diagnosis processing menu 148.

A protect program for limiting the number of use times or the expirationdate in the downloading information processing terminal 129 may be addedto the programs 41 and 45 and the data 143 a downloaded from thediagnosis processing server 125 into the information processing terminal129.

As indicated by arrow A in FIG. 10, the protect program may beautomatically executed just after completion of download, may access thediagnosis processing server 125 for undergoing the authentication checkprocess 149, and may erase, etc., the actual measurement data analysisprogram 141, the determination program 145, and the determinationcriterion data 143 a after use the stipulated number of times, therebylimiting the number of use times.

In the anomaly diagnosis apparatus 121 of the second embodimentdescribed above, to diagnose the presence or absence of an anomaly inthe machine installation 127 containing the sliding member, thediagnosis processing of the diagnosis program made up of the actualmeasurement data analysis program 141 and the determination program 145is performed in the information processing terminal 129 installed in theuser, so that the user is saved from having to transmit the actualmeasurement vibration data 131 recording sound or vibration produced bythe machine installation 127 containing the sliding member to bediagnosed to the manufacturer and the diagnosis processing can bespeeded up because of saving labor and time required for transmittingthe actual measurement vibration data 131 to the manufacturer.

The diagnosis processing server 125 to which the actual measurement dataanalysis program 141, the determination program 145, and thedetermination criterion data 143 a required for the diagnosis processingare uploaded is used to download the programs and the determinationcriterion data 143 a to the user's information processing terminal anddoes not execute the diagnosis processing itself and thereforeconcentrating of the diagnosis processing of a large number of users onone information processing apparatus can be circumvented.

Further, the actual measurement data analysis program 141, thedetermination program 145, and the determination criterion data 143 arequired for the diagnosis processing are downloaded into theinformation processing terminal 129 of the user via the network 123 andcan be introduced into any desired information processing terminal 129of the user if the information processing terminal 129 has apredetermined communication function and program execution performance,and the diagnosis processing can be left to any idle informationprocessing apparatus of the user.

Therefore, as the diagnosis processing is started promptly, it can alsobe speeded up.

Further, concentrating of the diagnosis processing of a large number ofusers on one information processing apparatus need not be considered asdescribed early, so that it can be expected that even an informationprocessing apparatus having a not so high computation processingcapability will perform comparatively rapid processing.

Therefore, as a system configuration limiting the computation processingcapability of the information processing terminal 129 in moderation insuch a manner that a popularly priced personal computer is adopted asthe information processing terminal 129, the system construction costcan be suppressed to a low cost and at the same time, the diagnosisprocessing can be speeded up.

In the anomaly diagnosis apparatus 121 of the embodiment, the Internetalready constructed as a wide-area network and also promoted tobroadband for realizing high-speed communications is used as the network123 for accessing the diagnosis processing server 125, so that there isno extra cost for constructing, improving, etc., a dedicated network andit is made possible for a large number of users to use the diagnosissystem easily and at low cost.

Since the anomaly diagnosis apparatus 121 of the embodiment has theauthentication program for allowing only the authorized user to downloadthe programs and the data, it is made possible for the diagnosisprocessing server 125 to manage the versions, etc., of the diagnosisprogram and the determination criterion data 143 a required by the userin more detail with the authentication program and the customer data inassociation with each other, for example; it is made possible to realizereliable downloading the optimum diagnosis program and the determinationcriterion data 143 a without placing any burden on the user accessingthe diagnosis processing server 125, and service as the manufacturer canbe enhanced.

Since the anomaly diagnosis apparatus 121 of the embodiment adds theprotect program for limiting the number of use times or the expirationdate in the information processing terminal 129 to the programs and thedata downloaded from the diagnosis processing server 125, illegalrepeated use or drain of the diagnosis processing program and thedetermination criterion data 143 a downloaded into the user can beprevented and erroneous use of the program and the data for anomalydiagnosis of a sliding member used with a different type of machineinstallation or the like can be prevented.

Therefore, illegal drain of the technology of the manufacturer can beprevented and the reliability of the diagnosis processing can beenhanced.

In the embodiment, the protect program automatically executed just aftercompletion of downloading the program and the data required for thediagnosis processing from the diagnosis processing server for limitingthe number of use times of the downloaded program and data is added tothe downloaded program and data. However, a similar advantage can alsobe provided by adopting a method of adding a protect program forlimiting the expiration date or the use time from the downloadcompletion time in such a manner that the downloaded program and dataare erased after the expiration of a given time since the downloadcompletion time, for example, or a step of accessing the diagnosisprocessing server and receiving authentication (process indicated by thearrow A shown in FIG. 10) when the downloaded program is executed inplace of the protect program for limiting the number of use times.

In the embodiment, the specific analysis method of the actualmeasurement vibration data in the actual measurement data analysisprogram, etc., is not limited to the envelope analysis, etc., shown inthe embodiment described above. Various waveform processing and analysismethods of shaping the vibration waveform data and revealing thewaveform features can be applied.

The specific processing method of the determination program is notlimited to the method in the embodiment described above either.

Further, the sliding member in the machine installation wherein thepresence or absence is diagnosed is not limited to a rolling bearingeither. Various machines and machine elements having rotary slidingparts or linear sliding parts, such as ball screws, linear guides, ormotors, for example, with unusual change made in operation sound when ananomaly of damage, etc., occurs in the sliding part can come under thesliding members according to the invention.

The types of data stored in the data storage section of the diagnosisprocessing server according to the invention, etc., are not limited tothose in the embodiment described above either. The types of datastored, etc., and putting data into databases can be improved whenevernecessary so that authentication of the authorized user and reporting ofthe measures responsive to the anomaly occurrence state can be executedspeedily.

The network to which the diagnosis processing server is connected is notlimited to the Internet. For example, a locally useful network, such asan already existing network using public lines or a cable TV network,may be selected.

FIG. 11 is a block diagram to show the configuration of a fifthembodiment of an anomaly diagnosis apparatus of a machine installationaccording to the invention, and FIG. 12 is a flowchart to show aprocedure of anomaly diagnosis processing executed by the apparatus inFIG. 11.

An anomaly diagnosis apparatus 201 of the embodiment diagnoses thepresence or absence of an anomaly caused by damage, etc., to a rollingbearing of a sliding member for a machine installation 203 installingone or more rolling bearings as sliding members causing vibration tooccur at the operation time.

It is known that the presence or absence of damage to a rolling bearingcan be determined based on whether or not an envelope signal ofvibration occurring from the bearing has a peak in a specific frequencycomponent.

The frequency can be calculated from the specifications of the bearing,and the relationship between damage points and frequencies as shown inFIG. 13 is known.

Therefore, the presence or absence of damage to a bearing can bedetermined simply by extracting only the frequency component from thefrequency spectrum of an envelope signal of vibration occurring from thebearing and comparing the frequency component with a reference valuedetermined in response to the effective value of the envelope signal.

The frequency is calculated after specification data is input when adiagnosis is made. To diagnose the same installation or machine, thedata resulting from the past calculation may be retained and be read foruse.

The anomaly diagnosis apparatus 201 of the embodiment includes a sensor211 for detecting sound or vibration, an amplifier 212, an AD converter213, and a diagnosis computer 214. The sensor 211 detects sound orvibration in the proximity of the diagnosis point of the machineinstallation 203 or machine to be diagnosed and uses a microphone, avibration acceleration sensor, etc.

A signal provided by the sensor 211 is amplified by the amplifier 212and then is converted into a digital signal by the AD converter 213 andthe digital signal is input to the diagnosis computer 214.

The diagnosis computer 214 contains a frequency analysis program andconducts an anomaly diagnosis. For frequency analysis used for ananomaly diagnosis based on sound or vibration data, various methods arewell known and therefore will not be discussed here.

An anomaly diagnosis method executed by the anomaly diagnosis apparatus201 of the embodiment is an improvement in determination procedure ofthe presence or absence of an anomaly and an abnormal point based onfrequency analysis data.

Next, the processing procedure in the anomaly diagnosis method executedby the anomaly diagnosis apparatus 201 will be discussed with referenceto FIG. 12.

To begin with, the user who wants to make an anomaly diagnosis detectssound or vibration data at the bearing use point by the sensor 211 (step231) and converts the detected analog data into digital data by the ADconverter 213 (step 232) and enters the digital data in the diagnosiscomputer 214.

The diagnosis computer 214 places the digital data in a file in apredetermined format (for example, a WAV file) (step 233). Hereinafter,the sound or vibration data placed in a file will be described simply as“digital data.”

The data may be converted into digital form using an AD conversionsection (not shown) contained in the diagnosis computer 214. The analogdata from the sensor 211 may be converted directly into digital form orthe data once recorded on magnetic tape, etc., may be converted intodigital form.

Next, the diagnosis computer 214 conducts frequency analysis of theinput digital data and determines the main frequency band of the inputdata (step 234). The diagnosis computer 214 determines the filter bandin response to the determined frequency band (step 235) and filters thedigital data (step 236). This step is executed to improve S/N of theinput sound or vibration data and need not be executed if S/N of theinput signal is sufficient.

Subsequently, envelope processing is executed (step 237) and frequencyanalysis processing is performed for the provided envelope data (step238) and the effective value is calculated (step 239). The effectivevalue provided at step 239 is used for calculation processing of areference value to determine an anomaly (step 242). The reference valueis found according to an expression such as reference value=effectivevalue+α (α: Variable) or reference value=effective value×β(β: Variable),for example.

At step 240, computation shown in FIG. 13 is performed based on thedesign specifications and use conditions of the bearing used at thediagnosis point, and the frequency component value occurring when ananomaly occurs at a specific point of the bearing is calculated. Thecalculated frequencies correspond to an inner ring flaw, an outer ringflaw, a rolling element flaw, and cage sound.

The frequency component values may be calculated before the step or whena similar diagnosis was conducted formerly, the data may be used. Thespecification data used for calculation is previously input.

Next, from the frequency spectrum data provided at step 238, inner ringflaw component Si (Zfi), outer ring flaw component So (Zfc), rollingelement flaw component Sb (2 fb), and cage component Sc (fc) areextracted in response to the frequency component values calculated atstep 240 (step 241), and the components are compared with the referencevalue calculated at step 242 (step 243). When all components are smallerthan the reference value, it is determined that the bearing is normal(step 244). When any component is larger than the reference value, thecorresponding part is determined abnormal, and a message is output (step245).

Next, a specific example of anomaly diagnosis is shown. For example,actual measurement frequency spectrum and envelope processing waveformsobtained as sound is recorded from a single row deep groove ball bearingunder the run conditions of fixed outer ring, 1500 revolutions of innerring per minute, and axial load 9.8 N are obtained as in FIGS. 14 and15. In FIG. 14, when the frequency components caused by the bearing areextracted and are compared with the reference value (−29.3 dB), thefrequency components are smaller than the reference value. That is, itis understood that the frequency components exceeding the referencevalue are not caused by the bearing and therefore it can be determinedthat the bearing is normal.

On the other hand, in the example in FIG. 15, the Zfc component causedby damage to the outer ring of the specific part is larger than thereference value (−19.5 dB) and therefore the abnormal sound isdetermined an outer ring flaw sound. In the example, referencevalue=effective value+10 dB.

Likewise, for a single row deep groove ball bearing (the part numberdiffers from that in FIG. 14, 15) under the run conditions of fixedouter ring, 2400 revolutions of inner ring per minute, and axial load9.8 N, waveform is obtained as in FIG. 16, for example. When thecomponents caused by the bearing are extracted and are compared with thereference value (−12.6 dB), the fc component caused by damage to thecage of the specific part is larger than the reference value and a cagesound anomaly is found. Also in the example, reference value=effectivevalue+10 dB.

Since the presence or absence of a peak of occurrence frequency isdetermined based on the match degree with the frequency caused by thebearing and the larger-than or smaller-than relation with the referencevalue, a diagnosis can be made if the peak level is small as in FIG. 16.

Thus, according to the anomaly diagnosis method according to the fifthembodiment of the invention, only specific frequency components areextracted from the frequency spectrum of an envelope signal of vibrationoccurring from the bearing and are compared with the reference valuedetermined in response to the effective value of the envelope signal, sothat the computation processing amount is lessened and speeding up thediagnosis processing can be promoted as compared with the related artmethod of checking to see if each frequency component is caused by thesliding member of the machine installation 203 in the descending orderof spectrum levels after frequency analysis. If the spectrum level peakof the frequency component caused by the sliding member of the machineinstallation 203 is small, the components can be extracted and thus ahighly accurate diagnosis is made possible.

In the description given above, the sound or vibration envelope signalis used to determine the presence or absence of a flaw of the bearing,but a signal representing sound or vibration can also be used directlyto diagnose the presence or absence of chatter sound of the bearing.

Next, processing in a sixth embodiment of an anomaly diagnosis apparatusof a machine installation according to the invention will be discussed.

The anomaly diagnosis apparatus of the sixth embodiment differs fromthat of the fifth embodiment in diagnosis program installed in diagnosiscomputer, but has the same hardware configuration as the anomalydiagnosis apparatus of the fifth embodiment shown in FIG. 11 andtherefore the hardware configuration will not be discussed again.

In the anomaly diagnosis apparatus of the sixth embodiment, sound orvibration data at the bearing use point is detected by a sensor 211 andthe detected analog data is converted into digital data by an ADconverter 213 to generate actual measurement digital data and thegenerated actual measurement digital data is input to a diagnosiscomputer 214. The diagnosis computer 214 performs appropriate analysisprocessing of frequency analysis, envelope analysis, and the like forthe input actual measurement digital data to generate actual measurementfrequency spectrum data and calculates the level difference between eacharbitrary data point and its immediately preceding data point and agradient to find a peak value for the generated actual measurementfrequency spectrum data.

The reason why the peak value can be found will be discussed.

Since the actual measurement frequency spectrum data is digital data,each frequency and spectrum level data exist discretely, of course.Thus, an expression of a curve (namely, function) is not required andthe level difference between data points can be used to find a peakvalue.

Specifically, in FIG. 17, the value difference between the level of onefrequency component (Y1) and the level one data before the frequencycomponent (Y0) is calculated and the result is obtained as thedifference data (δ=Y1−Y0).

When the sign of the difference data (δ) changes from plus to minus(zero in some cases), an inflection point is indicated and therefore thefrequency data and spectrum level data involved in the difference dataon which plus or minus is based is the peak value.

The peak value is calculated regardless of whether or not the crest issteep. By the way, the peak value required for diagnosis aims at onlysharp waveforms and thus data when the gradient formed by the frequencydata (x) and the spectrum level data (y) exceeds 1 (dy/dx>1) or issmaller than −1 (dy/dx<−1) is defined as the peak value.

The presence or absence of an anomaly and the anomaly part can bediagnosed by comparing with the frequency component caused by thebearing based on the peak value thus found.

FIG. 18 shows an example of extracting 10 points of large spectrumlevels in order using a peak value extracting method from frequencycomponents of large spectrum levels about the frequency analysis resultof data after undergoing AD conversion.

FIG. 19 shows an example of extracting 10 points of large spectrumlevels in order by the peak value extracting method of the embodiment.

As shown in FIG. 18, it is seen that in the peak value extracting methodin order from the frequency components of large spectrum levels, threepoints are extracted in the proximity of 140 Hz of valley points(values) of the spectrum although the level is high.

In contrast, in the peak value extracting method of the embodiment, asshown in FIG. 19, only peaks are extracted and the peaks in theproximity of 60 Hz can be extracted as 10 points of large spectrumlevels. Adopting the peak value extracting method, only the peaks areextracted, so that lost of necessary data as the valley points (values)of the spectrum are extracted can be prevented.

After the peak values are thus found for the actual measurementfrequency spectrum data, the presence or absence of an anomaly in thebearing is determined by comparing the peak values on the actualmeasurement frequency spectrum data relative to the frequency componentcaused by an anomaly in the bearing.

FIG. 20 is a flowchart to show the processing procedure of the anomalydiagnosis method executed by the anomaly diagnosis apparatus of thesixth embodiment.

In the figure, steps 231 to 237 are the same as those previouslydescribed with reference to FIG. 12 and therefore will not be discussedagain.

In the embodiment, envelope processing is executed (step 237) andfrequency analysis processing is performed for the provided envelopedata (step 258) and actual measurement frequency spectrum data isgenerated. For the generated actual measurement frequency spectrum data,the level difference between each arbitrary data point and itsimmediately preceding data point and a gradient are calculated tocalculate a peak value (step 261).

At step 262, computation shown in FIG. 13 is performed based on thedesign specifications and use conditions of the bearing used at thediagnosis point, and the frequency component value occurring when ananomaly occurs at a specific point of the bearing is calculated. Thecalculated frequencies correspond to an inner ring flaw, an outer ringflaw, a rolling element flaw, and cage sound. The frequency componentvalues may be calculated before the step or when a similar diagnosis wasconducted formerly, the data may be used. The specification data usedfor calculation is previously input.

Next, from the frequency spectrum data provided at step 258, inner ringflaw component Si (Zfi), outer ring flaw component So (Zfc), rollingelement flaw component Sb (2 fb), and cage component Sc (fc) areextracted in response to the frequency component values calculated atstep 262 (step 263), and the components are compared with the peak valuecalculated at step 261 (step 264). If there is no peak value in allcomponents, it is determined that the bearing is normal (step 265).

If there is the peak value in any component, the corresponding part isdetermined abnormal, and a message is output (step 266).

That is, if the peak value exists for the inner ring flaw component Si(Zfi), it is determined that the inner ring contains an anomaly of aflaw, etc. If the peak value exists for the outer ring flaw component So(Zfc), it is determined that the outer ring contains an anomaly of aflaw, etc. If the peak value exists for the rolling element flawcomponent Sb (2 fb), it is determined that the rolling element containsan anomaly of a flaw, etc. If the peak value exists for the cagecomponent Sc (fc), it is determined that the cage contains an anomaly ofa flaw, etc. If the peak value exists for a plurality of frequencycomponents, it can be determined that there are a plurality of abnormalpoints. For example, if the peak value exists for each of the inner ringflaw component Si (Zfi) and the outer ring flaw component So (Zfc), itcan be determined that the inner ring and the outer ring are abnormal.

Thus, according to the anomaly diagnosis method of the sixth embodiment,to conduct frequency analysis or envelope analysis on the signalprovided by detecting sound or vibration at the bearing use point andextract frequency peaks from the actual measurement frequency spectrumdata provided accordingly, for the actual measurement frequency spectrumdata, the level difference between each arbitrary data point and itsimmediately preceding data point and a gradient are calculated to find apeak value, so that extracting of a valley can be circumvented andhigher-accuracy diagnosis is made possible as compared with the case ofextracting from frequency components of large spectrum levels.

In the embodiment, the signal generated from the sliding member of amachine installation 203 is processed, but the invention can also beapplied to a mode wherein a shape signal of roughness, etc., isconverted into a digital amount.

In the embodiment, the peak value is found for the waveform afterfrequency analysis or envelope analysis, but the peak value may beextracted from actual measurement digital data into which data ofdetected sound or vibration occurring from the bearing is converted.

FIG. 21 is a block diagram to show a schematic configuration of aseventh embodiment of an anomaly diagnosis apparatus according to theinvention. Components common to those of the anomaly diagnosis apparatus201 of the fifth embodiment shown in FIG. 11 are denoted by the samereference numerals in FIG. 21.

An anomaly diagnosis apparatus 205 of the seventh embodiment detectssound or vibration at a bearing use point by a sensor 211 and convertsdetected analog signal into digital data by an AD converter 213 togenerate actual measurement digital data and inputs the generated actualmeasurement digital data to a diagnosis computer 214. The diagnosiscomputer 214 includes a pointing device 215 such as a mouse and an ADconverter 216 for converting X and Y coordinate data output from thepointing device 215 into digital data.

The diagnosis computer 214 further includes a graphical user interface217, enabling the user to use the pointing device 215 to select anydesired time domain out of a time domain waveform based on waveform dataafter AD conversion or any desired frequency domain out of a frequencydomain waveform based on waveform data provided by conducting frequencyanalysis of the waveform data after AD conversion, as described below.

The waveform data after AD conversion or the waveform data provided byconducting frequency analysis of the waveform data after AD conversionis displayed on a monitor (not shown) of the diagnosis computer 214.While seeing the display, the user operates the pointing device 215 tospecify any desired time domain waveform or any desired frequency domainwaveform.

When the user uses the pointing device 215 to select any desired timedomain part out of the waveform data visually displayed on the monitor,appropriate analysis processing of frequency analysis, envelopeanalysis, etc., is performed for the data in the time domain part togenerate actual measurement frequency spectrum data and visuallydisplays the actual measurement frequency spectrum data on the monitor.Next, when the user uses the pointing device 215 to specify any desiredfrequency domain part in the frequency domain waveform visuallydisplayed on the monitor, the diagnosis computer 214 assumes thefrequency domain part to be a filter band and filters actual measurementdigital data to generate new actual measurement frequency spectrum data.

After the actual measurement frequency spectrum data is generated, thelevel difference between each data point and its immediately precedingdata point and a gradient are calculated to find a peak value in theascending order of the frequencies. After the peak value is found, it iscompared with the peak value on the actual measurement frequencyspectrum data for the frequency component occurring when a specific partof the bearing is abnormal, and the presence or absence of an anomaly inthe bearing is diagnosed.

Here, a specific example will be discussed with reference to theaccompanying drawings.

FIG. 22 is a raw signal data waveform chart after AD conversion. Theuser uses the pointing device 215 to drag a portion which seems to beless affected by noise, etc., in the signal data waveform, there byselecting any desired time domain.

The hatched portion in the figure is the selected portion (time domainpart). After the time domain part is selected, a raw waveform of signaldata in the selected range is visually displayed automatically or bysimple operation (see FIG. 23). If necessary, further range selectioncan also be performed. Envelope processing and frequency analysis areperformed for the selected signal data automatically or by simpleoperation and a frequency spectrum (see FIG. 24) and an envelopefrequency spectrum (see FIG. 25) are visually displayed.

If the user uses the pointing device 215 to drag any desired frequencydomain (see FIG. 26) in the frequency spectrum, filtering with theselected range as the filter band, envelope processing, and frequencyanalysis are performed automatically or by simple operation and afrequency spectrum (see FIG. 27) and an envelope frequency spectrum (seeFIG. 28) are visually displayed.

Thus, according to the anomaly diagnosis method of the seventhembodiment of the invention, the user can use the pointing device 215such as a mouse to select the analysis range and the filtering range forany desired waveform range in the time waveform after AD conversion andthe spectrum waveform after frequency analysis, so that a signal havinga high S/N ratio can be extracted by simple operation andhigher-accuracy diagnosis is made possible.

At this time, the user can also listen to sound data after beingfiltered and can also make a determination by the hearing sense.

FIG. 29 is a flowchart to show a diagnosis processing procedure of ananomaly diagnosis method executed by an eighth embodiment of an anomalydiagnosis apparatus according to the invention. The basic hardwareconfiguration of the apparatus may be similar to that of the apparatusof the fifth embodiment shown in FIG. 11 and therefore the hardwareconfiguration will not be discussed again.

In an anomaly diagnosis method according to the eighth embodiment, soundor vibration produced by a machine installation containing a slidingmember is analyzed, thereby diagnosing the presence or absence of ananomaly in the sliding member, etc., of the machine installation.

The expression “sliding member, etc., of machine installation” mentionedhere is used to mean that the sliding member of the machine installationand a sliding member relevant member joined to the sliding member orsupporting the sliding member in the machine installation are included.

For example, bearings, ball screws, linear guides, motors, etc., comeunder the sliding members.

In the anomaly diagnosis method according to the eighth embodiment,first a signal representing sound or vibration produced by the slidingmember, etc., of the machine installation is detected and actualmeasurement frequency spectrum data of a frequency spectrum of thedetected signal or its envelope signal is generated. The laterprocessing is performed according to the procedure shown in FIG. 29.

That is, first a basic frequency component comparison process ofchecking whether or not the frequency at an appearance point of a peakequal to or higher than a reference level on the actual measurementfrequency spectrum data matches the basic frequency at which a peakappears because of an anomaly in a specific part of the sliding member,etc., is executed (step 301) and when the frequency at the appearancepoint of the peak equal to or higher than the reference level on theactual measurement frequency spectrum data does not match the basicfrequency in the basic frequency component comparison process, thesliding member, etc., is diagnosed as no anomaly (step 302).

When the frequency at the appearance point of the peak equal to orhigher than the reference level on the actual measurement frequencyspectrum data matches the basic frequency in the basic frequencycomponent comparison process at step 301, the process advances to step303 and a low-frequency component comparison process of checking thepresence or absence of a frequency component having a peak equal to orhigher than the reference level in a low-frequency range equal to orless than the basic frequency on the actual measurement frequencyspectrum data is executed.

When the actual measurement frequency spectrum data does not have a peakequal to or higher than the reference level in the low-frequency rangeequal to or less than the basic frequency in the low-frequency componentcomparison process at step 303, the sliding member, etc., is diagnosedas an anomaly in the specific part (step 304).

On the other hand, when the actual measurement frequency spectrum datahas a peak equal to or higher than the reference level in thelow-frequency range equal to or less than the basic frequency in thelow-frequency component comparison process at step 303, the processadvances to step 305 and a harmonic component comparison process ofdetermining whether or not the harmonic of the frequency componenthaving the peak equal to or higher than the reference level in thelow-frequency range equal to or less than the basic frequency matchesthe basic frequency is executed.

When the harmonic of the frequency component having the peak equal to orhigher than the reference level in the low-frequency range equal to orless than the basic frequency does not match the basic frequency in theharmonic component comparison process at step 305, the sliding member,etc., is diagnosed as an anomaly in the specific part (step 306); whenthe harmonic matches the basic frequency, the sliding member, etc., isdiagnosed as no anomaly in the specific part (step 307).

The situation in which the peak level on the actual measurementfrequency spectrum data exceeds the reference level is caused to occurnot only in the case where an anomaly of damage, etc., occurs in thesliding member, etc., but also by any other factor of overlap offrequency components of rotation components, etc., of the slidingmember, etc., contained in the machine installation, the effect ofharmonic, etc.

Thus, if the sliding member, etc., is diagnosed as an anomaly basedsimply on the fact that the basic frequency at which a peak appearsbecause of an anomaly in a specific part of the sliding member, etc.,matches the peak on the actual measurement frequency spectrum data asthe result of making comparison therebetween, in fact the peak equal toor higher than the reference level is caused by any other factor and noanomaly may occur in the sliding member, etc.; there is a risk ofincurring degradation of reliability of the diagnosis.

However, as in the embodiment, when a match is found in the basicfrequency component comparison process, if the low-frequency componentcomparison process and the harmonic component comparison process arefurther executed, whether or not the peak equal to or higher than thereference level in the basic frequency on the actual measurementfrequency spectrum data is caused by any other factor of overlap offrequency components of rotation components, etc., of the slidingmember, etc., the effect of harmonic, etc., for example, rather than ananomaly of damage, etc., in the sliding member.

Thus, erroneous diagnosis of assuming that the peak caused by any otherfactor of overlap of frequency components of rotation components, etc.,of the sliding member, etc., the effect of harmonic, etc., is caused byan anomaly in the sliding member, etc., can be circumvented and thereliability of diagnosing the presence or absence of an anomaly in thesliding member, etc., can be improved.

Next, waveform charts of actual measurement frequency spectrum of thesliding member to be diagnosed and a diagnosis process according to theembodiment for each waveform chart will be discussed.

FIG. 30 is a schematic representation of a waveform when the diagnosisprocessing in FIG. 29 is executed for a damaged inner ring of a rollingbearing as the sliding member. In the basic frequency componentcomparison process, it is recognized that the peak equal to or higherthan the reference level matches the basic frequency of the peak causedby the inner ring damage (Zfi: 89.9 Hz). Thus, the low-frequencycomponent comparison process is executed. In the low-frequency componentcomparison process, it is recognized that a high-level component is notfound in the low-frequency range equal to or less than the basicfrequency (Zfi: 89.9 Hz) and therefore it is not assumed that occurrenceof the large peak in the basic frequency is caused by any other factormentioned above, and the process advances to step 304 and the inner ringis diagnosed as an anomaly.

FIG. 31 is a schematic representation of a waveform when the diagnosisprocessing in FIG. 29 is executed for an undamaged outer ring of arolling bearing as the sliding member. In the basic frequency componentcomparison process, it is recognized that the peak equal to or higherthan the reference level matches the basic frequency of the peak causedby the outer ring damage (Zfc: 194.7 Hz). Thus, the low-frequencycomponent comparison process is executed. In the low-frequency componentcomparison process, it is recognized that a high-level component f1(64.5 Hz) exists in the lower-frequency range than the basic frequency(Zfc: 194.7 Hz) and therefore the harmonic component comparison processat step 305 is executed.

In the harmonic component comparison process, it is recognized that theharmonic of the high-level component f1 (64.5 Hz) in the low-frequencyrange roughly matches the basic frequency and therefore it is assumedthat occurrence of the large peak in the basic frequency is caused byany other factor mentioned above, and the process advances to step 307and the outer ring is diagnosed as no anomaly.

FIG. 32 is a schematic representation of a waveform when the diagnosisprocessing in FIG. 29 is executed for a damaged inner ring of a rollingbearing as the sliding member. In the basic frequency componentcomparison process, it is recognized that the peak equal to or higherthan the reference level matches the basic frequency of the peak causedby the inner ring damage (Zfi: 150.7 Hz). Thus, the low-frequencycomponent comparison process is executed. In the low-frequency componentcomparison process, a higher-level component f2 (30.1 Hz) than thereference level is found in the low-frequency range equal to or lessthan the basic frequency (Zfi: 150.7 Hz). Therefore, the process goes tostep 305 and the harmonic component comparison process is executed.

In the harmonic component comparison process, it is recognized that theharmonic of the high-level component f2 (30.1 Hz) in the low-frequencyrange roughly matches the basic frequency, but the level of the basicfrequency is higher than the level of the component f2 and therefore itis not assumed that occurrence of the large peak in the basic frequencyis caused by any other factor mentioned above, and the process advancesto step 306 and the inner ring is diagnosed as an anomaly.

As described above, if the low-frequency component comparison processand the harmonic component comparison process are added to the basicfrequency component comparison process and the presence or absence ofthe effect of any other factor of harmonic, etc., is examined,higher-reliability diagnosis can be realized.

FIG. 33 is a block diagram to show a schematic configuration of a ninthembodiment of an anomaly diagnosis method and apparatus of a machineinstallation according to the invention. FIG. 34 is a flowchart to showa diagnosis processing procedure of the anomaly diagnosis apparatus of amachine installation shown in FIG. 33.

To being with, the schematic configuration of an anomaly diagnosisapparatus 401 of the ninth embodiment will be discussed with referenceto FIG. 33 and then the anomaly diagnosis method of a machineinstallation executed by the anomaly diagnosis apparatus 401 will bediscussed in detail.

The anomaly diagnosis apparatus 401 of a machine installation of theninth embodiment includes vibration detection means 405 for outputtingan analog electric signal responsive to sound or vibration produced by asliding member of the machine installation to be diagnosed,amplification member 407 for amplifying the signal output by thevibration detection means 405, AD conversion means 409 for convertingthe analog signal amplified by the amplification member 407 into adigital signal to generate actual measurement digital data, andcomputation processing means 413 for diagnosing the presence or absenceof an anomaly in a specific part of the sliding member of the machineinstallation based on the actual measurement digital data output by theAD conversion means 409.

The embodiment assumes that the sliding member of the machineinstallation is a rolling bearing. Wearing of and damage to inner andouter rings, rolling element, a cage, etc., making up the rollingbearing are diagnosed based on sound or vibration when the rollingbearing is driven.

In the embodiment, the expression “sound or vibration of sliding memberof machine installation” is used to mean that AE (Acoustic Emission)when the sliding member of the machine installation is driven iscontained.

The computation processing means 413 is a diagnosis computer forperforming computation processing of previously stored processing dataand actual measurement digital data received from the AD conversionmeans 409 according to a diagnosis program.

The computation processing means 413 performs appropriate analysisprocessing of frequency analysis, envelope analysis, and the like forthe actual measurement digital data output by the AD conversion means409 to generate actual measurement frequency spectrum data and diagnosesthe presence or absence of an anomaly in a specific part of the slidingmember 403 of the machine installation based on the presence or absenceof a peak on the actual measurement frequency spectrum data for thefirst-order, second-order, fourth-order value of frequency componentoccurring when the specific part of the machine installation 403containing the sliding member is abnormal.

The described anomaly diagnosis apparatus 401 of the machineinstallation performs processing according to the procedure shown inFIG. 34.

First, the vibration detection means 405 detects sound or vibrationproduced by the machine installation 403 containing the sliding member(step S431). Next, a signal provided by the amplification means 407 isconverted into a digital signal by the AD conversion means 409 (stepS432) and the digital signal is passed to the computation processingmeans 413.

The computation processing means 413 puts the signal received from theAD conversion means 409 into a digital file in a file format such as aWAV file, for example, (step S433). If necessary, filtering is performedfor removing, etc., extra signal to generate actual measurement digitaldata.

In the embodiment, as the filtering, predetermined processing isperformed for the input signal by a filtering program previously builtin the computation processing means 413, and the filtering is made up ofa filter band selection step of presetting the frequency range to becut, etc., (step S434) and a filtering step of cutting extra signalaccording to the selected filter band (step S435).

The filtering at steps S434 and S435 is executed to improve the S/Nratio of the collected data and need not be executed if the S/N ratio ofthe input signal is sufficient.

Next, analysis processing of frequency analysis, envelop analysis, andthe like is performed for the generated actual measurement digital data(steps S436 and S107), and actual measurement frequency spectrum data d1representing sound or vibration detected from the machine installation403 containing the sliding member is obtained (step S438).

The obtained actual measurement frequency spectrum data d1 is waveformw1 shown in FIG. 35.

This waveform w1 is provided by rotating the inner ring at a speed of150 rpm with the outer ring fixed in the rolling bearing as the slidingmember of the machine installation.

Further, the computation processing means 413 diagnoses the presence orabsence of an anomaly in a specific part of the machine installation 403containing the sliding member based on the presence or absence of a peakon the actual measurement frequency spectrum data d1 for thefirst-order, second-order, fourth-order value of frequency componentoccurring when the specific part of the machine installation 403containing the sliding member is abnormal (step S439).

As for the bearing of the sliding member of the machine installation,the frequency component values occurring when specific parts areabnormal are determined in response to the design specifications and useconditions of the bearing, as shown in FIG. 13.

The computation processing means 413 previously stores as referencevalues the first-order, second-order, and fourth-order values offrequency component occurring when each specific part shown in FIG. 13is abnormal for the machine installation 403 containing the slidingmember, and executes step S439 based on the reference values.

At step S439, specifically, according to a procedure shown in FIG. 36, acomparison process of checking the presence or absence of a peak on theactual measurement frequency spectrum data d1 for the first-order,second-order, fourth-order value of frequency component occurring wheneach specific part of the machine installation 403 containing thesliding member is abnormal is executed. If it is recognized that a peakexists on the actual measurement frequency spectrum data d1 in two ormore frequency components as both the first-order value and thesecond-order value of the frequency component match a peak on the actualmeasurement frequency spectrum data d1 (steps S451 and S452) or as boththe second-order value and the fourth-order value match a peak on theactual measurement frequency spectrum data d1 although the first-ordervalue of the frequency component does not match a peak on the actualmeasurement frequency spectrum data d1 (steps S461 and S462), thesliding member is diagnosed as an anomaly in the specific part (stepS471).

On the other hand, if a peak exists on the actual measurement frequencyspectrum data d1 in one or no frequency component, the possibility thata peak may happen to be formed because of vibration, etc., caused byanomaly in any other part as noise is high, and the sliding member isdiagnosed as no anomaly (step S481).

FIG. 37 shows three frequency components of first-order value Q1,second-order value Q2, and fourth-order value Q4 of frequency componentsoccurring due to damage to the outer ring of a specific part by dashedlines in the waveform w1 provided by rotating the inner ring at a speedof 150 rpm with the outer ring fixed in the rolling bearing as thesliding member of the machine installation.

FIG. 35 shows all frequency components of first-order value Q1 tohigh-order Qn of frequency components occurring due to damage to theouter ring by dashed lines in similar waveform w1.

In the anomaly diagnosis method executed by the anomaly diagnosisapparatus 401 of the machine installation of the ninth embodimentdescribed above, the comparison process of checking the presence orabsence of a peak on the actual measurement frequency spectrum data d1corresponding to frequency components occurring when each specific partof the machine installation 403 containing the sliding member isabnormal is limited to three times of the first-order, second-order, andfourth-order values of the frequency components occurring when thespecific part of the machine installation 403 containing the slidingmember is abnormal and therefore the computation processing amount inthe comparison process is drastically decreased as compared with therelated art case where the comparison process is repeated for all of alarge number of frequency components of first-order to high-orderfrequency components, for example, as shown in FIG. 35.

Thus, the load on the computation processing means 413 in analyzing thevibration signal detected from the machine installation 403 containingthe sliding member is lightened drastically and the diagnosis work canbe speeded up. Since the computation processing amount is decreased, itis made possible to use an inexpensive computer having a low computationprocessing capability as the computer used as the computation processingmeans 413 and it is also made possible to decrease the apparatus cost.

Further, if a determination is made based only on the first-ordercomponent of frequency components occurring under abnormal condition,there is a possibility of making an erroneous diagnosis as a peak on thecorresponding actual measurement frequency spectrum happens to shift orgrow due to the effect of noise, etc.

However, to execute the comparison process three times of thefirst-order, second-order, and fourth-order values of the frequencycomponents occurring when the specific part of the machine installation403 containing the sliding member is abnormal as described above, thereis almost no probability that the process will receive the effect ofnoise, etc., three times, and the reliability of the diagnosis can beimproved.

Preferably, after generating the actual measurement frequency spectrumdata d1, the computation processing means 413 calculates an effectivevalue f1 of the actual measurement frequency spectrum data d1, sets athreshold value t1 based on the effective value f1, and handles eachpeak on the actual measurement frequency spectrum data d1 for thefirst-order value Q1, the second-order value Q2, and the fourth-ordervalue Q4 of the frequency components occurring when the specific part ofthe machine installation 403 containing the sliding member is abnormalas the effective peak only when the peak exceeds the threshold value t1.

In FIG. 38, the effective value f1 and the threshold value t1 arewritten into a waveform w2 of the actual measurement frequency spectrumdata d1 provided by rotating the inner ring at a speed of 150 rpm withthe outer ring fixed in the rolling bearing as the sliding member of themachine installation. First-order value q1, second-order value q2, andfourth-order value q4 of the frequency components occurring due to aflaw of the rolling element of the rolling bearing as the sliding memberof the machine installation are written into the waveform w2 by dottedlines.

In this case, the effective value f1 is provided as the average level ofamplitude of the waveform w2 is calculated, and is −8.5 dB

The threshold value t1 is 1.5 dB because of settingt1=(f1+10 dB)  (1)

In this example, it is shown that all of the three peaks correspondingto the first-order value, the second-order value, and the fourth-ordervalue of the frequency components occurring due to the flaw of therolling element are greater than the threshold value t1 and thecomparison process is required.

When it is thus made possible to select significant peaks based on thethreshold value t1, if an extraction process of extracting effectivepeaks based on the threshold value t1 is performed, for example, beforea computation process for comparison for the peaks on the actualmeasurement frequency spectrum data corresponding to the first-ordervalue, the second-order value, and the fourth-order value of thefrequency components occurring when the specific part of the machineinstallation 403 containing the sliding member is abnormal is executed,waste of executing the comparison process for insignificant peaks can beavoided and the load of the computation processing amount is furthermorelightened and speeding up the diagnosis process can be promoted.

In the embodiments described above, diagnosing the presence or absenceof an anomaly of damage to each specific part has been shown.

However, if appropriate analysis processing of frequency analysis,envelope analysis, and the like is performed for the actual measurementdigital data to generate the actual measurement frequency spectrum datad1 as described above, it is advisable to calculate the effective valuef1 of the actual measurement frequency spectrum data d1, set thecalculated effective value as reference level L0, and estimate themagnitude of damage to the specific part of the sliding member of themachine installation causing an anomaly to occur from the value of leveldifference l between level Lh on the actual measurement frequencyspectrum data d1 corresponding to the first-order Q1 of the frequencycomponents occurring when the specific part of the machine installation403 containing the sliding member is abnormal and the reference levelL0, for example, as shown in FIG. 39.

FIG. 40 shows the relationship between the magnitude of peel and thelevel difference between each peak appearing on the actual measurementfrequency spectrum data d1 and the reference level when peel of damageto a raceway ring occurs in the rolling bearing as the sliding member ofthe machine installation.

Thus, generally the level difference grows in proportion to themagnitude of damage and therefore if the level difference between eachpeak appearing on the actual measurement frequency spectrum data d1 andthe reference level is found, the magnitude of damage can be estimated.

Moreover, growing of the peak level on the actual measurement frequencyspectrum caused by damage to the machine installation 403 containing thesliding member becomes most noticeable at the peak corresponding to thefirst-order value of the frequency components caused by the anomaly.

Thus, the level difference between the level on the actual measurementfrequency spectrum data corresponding to the first-order value of thefrequency components occurring when the specific part of the machineinstallation 403 containing the sliding member is abnormal and theeffective value of the actual measurement frequency spectrum data iscalculated, whereby the magnitude of the damage can be estimatedefficiently by performing minimum computation processing, and thedamaged part replacement time is determined from the estimated magnitudeof the damage, so that excessive parts replacement and maintenance arecircumvented and it is made possible to reduce the upkeep cost in themachine and the installation containing the sliding member of themachine installation.

The average value of the actual measurement frequency spectrum data d1may be adopted as the reference level L0 in place of the effective valuef1.

The machine installation and the sliding member diagnosed by the anomalydiagnosis method and apparatus of the machine installation of theinvention are not limited to the rolling bearings shown in theembodiments described above.

Any machine installation can be diagnosed if it is a machineinstallation or a machine containing one or more sliding members whereinvibration occurs due to rotary sliding or linear sliding of the slidingmember. The sliding members also include ball screws, linear guides,motors, etc., for example, in addition to the rolling bearings.

The sliding member of the machine installation can be diagnosed withoutbeing removed from the machine or the installation if sound or vibrationoccurring when the sliding member of the machine installation is rotatedcan be detected by predetermined vibration detection means even with thesliding member built in the machine or the installation.

FIG. 41 is a block diagram to show a schematic configuration of a tenthembodiment of an anomaly diagnosis apparatus of a machine installationaccording to the invention. FIG. 42 is a flowchart to show a diagnosisprocessing procedure of the anomaly diagnosis apparatus of a machineinstallation shown in FIG. 41.

An anomaly diagnosis apparatus 501 of a machine installation of theembodiment includes vibration detection means 505 for outputting ananalog electric signal responsive to sound or vibration produced by amachine installation containing a sliding member 503 to be diagnosed,amplification member 507 for amplifying the signal output by thevibration detection means 505, AD conversion means 509 for convertingthe analog signal amplified by the amplification member 507 into adigital signal to generate actual measurement digital data, samplingreference setting means 511 for setting a reference value to exclude anarea where the effect of noise is large from the output signal of thevibration detection means 505, input means 513 for inputting necessaryinformation for the sampling reference setting means 511 to set thereference value, sampling means 515 for extracting effective actualmeasurement data with an area where the effect of noise is largeexcluded from the output signal of the vibration detection means 505based on the reference value set in the sampling reference setting means511, and computation processing means 517 for diagnosing the presence orabsence of an anomaly in a specific part of the machine installationcontaining the sliding member 503 based on the effective actualmeasurement data extracted by the sampling means 515.

The embodiment assumes that the machine installation containing thesliding member 503 is a rolling bearing. Wearing of and damage to innerand outer rings, rolling element, a cage, etc., making up the rollingbearing are diagnosed based on sound or vibration when the rollingbearing is driven.

In the embodiment, the expression “sound or vibration of the machineinstallation containing the sliding member 503” is used to mean that AE(Acoustic Emission) when the sliding member of the machine installationis driven is contained.

The output signal of the vibration detection means 505 is passed throughthe amplification member 507 and the AD conversion means 509 and becomesa waveform with voltage v changing as shown in FIG. 43. In FIG. 43, thehorizontal axis represents the elapsed time and the vertical axisrepresents the voltage value of the magnitude proportional to themagnitude of sound or vibration of the machine installation containingthe sliding member 503 (units: v).

In FIG. 43, A point and B point indicate points where the voltage valuebecomes excessive because of noise.

The sampling reference setting means 511 sets the reference value toexclude an area where the effect of noise is large from the outputsignal of the vibration detection means 505 based on information (data)previously specified by the input means 513.

In the embodiment, the reference value is the reference voltage todetect the A point and B point where the voltage value is excessivebecause of noise from the detection waveform in FIG. 43, and is 1.5 v.

The reference voltage generally is a larger value than the peak voltagevalue of the waveform produced because of an anomaly in the machineinstallation containing the sliding member and is set based on thereference voltage value specification (numeric input) from the inputmeans 513.

The sampling means 515 extracts the effective actual measurement dataprovided by excluding an area containing an excessive value exceedingthe reference value set in the sampling reference setting means 511because of the effect of noise from the detection data shown in FIG. 43.

In FIG. 43, the data in the section from A point to B point (timedomain) is extracted as the effective actual measurement data.

However, in the A point, the sampling is started on the falling edge ofthe peak waveform, thereby excluding the effect of noise. In the Bpoint, the sampling is terminated on the rising edge of the peakwaveform, thereby excluding the effect of noise.

The computation processing means 517 is a diagnosis computer forperforming computation processing of previously stored processing dataand effective actual measurement: data received from the sampling means515 according to a diagnosis program.

The computation processing means 517 performs appropriate analysisprocessing of frequency analysis, envelope analysis, and the like forthe effective actual measurement data output by the sampling means 515to generate actual measurement frequency spectrum data and diagnoses thepresence or absence of an anomaly in a specific part of the machineinstallation containing the sliding member 503 based on the presence orabsence of a peak on the actual measurement frequency spectrum data forthe frequency components occurring when the specific part of the machineinstallation containing the sliding member 503 is abnormal.

FIG. 44 is a waveform chart of the actual measurement frequency spectrumdata calculated by the computation processing means 517.

The described anomaly diagnosis apparatus 501 of the machineinstallation performs processing according to the procedure shown inFIG. 42.

First, the vibration detection means 505 detects sound or vibrationproduced by the machine installation containing the sliding member 503(step S531). Next, a signal provided by the amplification means 507 isconverted into a digital signal by the AD conversion means 509 (stepS532) and the digital signal is passed to the sampling means 515.

The sampling means 515 extracts effective actual measurement data withan area containing an excessive value because of the effect of noiseexcluded from the signal received from the AD conversion means 509 basedon the reference value set in the sampling reference setting means 511,and passes the extracted effective actual measurement data to thecomputation processing means 517 (step S533).

The computation processing means 517 performs analysis processing ofenvelop analysis, FFT analysis, and the like for the effective actualmeasurement data input from the sampling means 515 (steps S534 andS535), and provides envelope FFT spectrum data as actual measurementfrequency spectrum data representing sound or vibration detected fromthe machine installation containing the sliding member 503 (step S536).

The provided envelope FFT spectrum data is spectrum waveform w1 shown inFIG. 44. It is applied when an outer ring is damaged in the rollingbearing as the machine installation containing the sliding member 503.

Further, the computation processing means 517 diagnoses the presence orabsence of an anomaly in a specific part of the machine installationcontaining the sliding member 503 based on the presence or absence of apeak on the spectrum waveform w1 for the frequency components occurringwhen the specific part of the machine installation containing thesliding member 503 is abnormal (step S537).

As for the bearing of the machine installation containing the slidingmember, the frequency component values occurring when specific parts areabnormal are determined in response to the design specifications and useconditions of the bearing, as shown in FIG. 13.

The computation processing means 517 previously stores as diagnosisreference values the frequency components occurring when each specificpart shown in FIG. 13 is abnormal for the machine installationcontaining the sliding member 503, and executes step S537 based on thediagnosis reference values.

At step S537, specifically, a comparison process of checking thepresence or absence of a peak on the spectrum waveform w1 for thefrequency components occurring when the specific part is abnormal foreach specific part of the machine installation containing the slidingmember 503 is executed. If the waveform has a peak in the frequencycomponent occurring when the specific part is abnormal and the peak is agiven level or higher, the sliding member is diagnosed as an anomaly inthe specific part.

On the other hand, although the waveform has a peak in the frequencycomponent occurring when the specific part is abnormal, if the peak is agiven level or lower, the sliding member is diagnosed as no anomaly inthe specific part.

Since the spectrum waveform w1 shown in FIG. 44 has a large peak inbasic frequency component (Zfc) caused by outer ring damage of therolling bearing, the bearing is diagnosed as an anomaly in the outerring.

FIG. 45 shows spectrum waveform w2 of envelope FFT spectrum datagenerated as actual measurement digital data provided by the ADconversion means 509 for the same rolling bearing having the damagedouter ring is input directly to the computation processing means 517without removing noise by the sampling means 515 and is subjected toanalysis processing of envelop analysis, FFT analysis, and the like.

Thus, if the outer ring is damaged, a peak noticeable in the basicfrequency component (Zfc) caused by outer ring damage does not appear inthe spectrum waveform w2 where noise is not removed, and there is a fearof overlooking occurrence of the above.

As seen from the description given above, in the anomaly diagnosisapparatus 501 of the machine installation of the embodiment, thesampling means 515 automatically executes removal of the noise componentfrom the actual measurement data detected by the vibration detectionmeans 505 from the machine installation containing the sliding member503 based on the reference value set in the sampling reference settingmeans 511.

Therefore, the person in charge of diagnosis for managing the anomalydiagnosis apparatus 501 of the machine installation need not check theactual measurement data to remove the noise component each time, and thenecessity for interrupting processing of the anomaly diagnosis apparatus501 of the machine installation to check the actual measurement datadoes not occur either.

That is, it is not necessary to interrupt processing of the anomalydiagnosis apparatus 501 of the machine installation to remove the noisecomponent from the actual measurement data detected from the machineinstallation containing the sliding member 503, and the diagnosisprocessing can be speeded up.

Further, the noise component is removed uniformly by machine processingbased on the reference value, so that the skill degree of the person incharge of diagnosis does not affect the noise component removal rate.Therefore, the noise component removal rate can be made constant and thereliability of the diagnosis can be stabilized.

Since the person in charge of diagnosis need not check the actualmeasurement data to remove the noise component, an output unit fordisplaying the actual measurement data detected by the vibrationdetection means 505 in such a manner that the person in charge ofdiagnosis can check the actual measurement data can be omitted. As theoutput unit is omitted, the configuration of the apparatus can besimplified and the apparatus cost can also be decreased.

In the anomaly diagnosis apparatus of the machine installation of theinvention, the reference value set in the sampling reference settingmeans to exclude an area where the effect of noise is large is notlimited to the voltage value shown in the embodiment. For example, if itis known that noise is carried periodically, the sampling timing may beadjusted by a delay circuit, etc., matching the noise carrying timing,thereby eliminating the effect of noise.

The reference value setting method in the sampling reference settingmeans is not limited to the method of directly specifying the referencevalue from the input means 513 as in the embodiment described above.

For example, the sampling reference setting means 511 may include acomputation processing function of calculating the average level and theeffective value of the electric signals detected by the vibrationdetection means 505 and may automatically set the reference value fromthe calculation results, the operation timing of the vibration detectionmeans 505, etc., and a predetermined constant, etc.

If the sampling reference setting means 511 thus automatically sets thereference value, the data entry operation of the person in charge ofdiagnosis in the anomaly diagnosis apparatus 501 of the machineinstallation to set the reference value is decreased and the load on theperson in charge of diagnosis is lightened and at the same time, therequired time for the data entry operation is saved, so that speeding upof the processing can be ensured.

The machine installation containing the sliding member diagnosed by theanomaly diagnosis apparatus of the machine installation of the inventionis not limited to the rolling bearings shown in the embodiment describedabove. Machine installations containing various sliding members otherthan bearings can be diagnosed. The machine installation containing thesliding member can be diagnosed without being removed from the machineor the installation if sound or vibration occurring when the machineinstallation containing the sliding member is rotated can be detected bypredetermined vibration detection means even with the machineinstallation containing the sliding member built in the machine or theinstallation.

In the embodiment, the process of excluding an area where the effect ofnoise is large is executed for the vibration signal detected by thevibration detection means after the AD conversion processing performsprocessing, but the process of excluding an area where the effect ofnoise is large can also be executed before the AD conversion isperformed.

While specific embodiments of the invention have been described indetail, it will be obvious to those skilled in the art that variouschanges and modifications may be made in the invention without departingfrom the spirit and scope thereof.

The application is based on Japanese patent application filed on Nov. 6,2000 (2000-337675), Japanese patent application filed on Oct. 23, 2001(2001-325003), Japanese patent application filed on Dec. 6, 2000(2000-371747), Japanese patent application filed on Oct. 23, 2001(2001-324980), Japanese patent application filed on Oct. 25, 2001(2001-327742), and Japanese patent application filed on Oct. 23, 2001(2001-325004), the contents of which are taken in here as references.

INDUSTRIAL APPLICABILITY

As described above, according to the anomaly diagnosis apparatus of themachine installation of the invention described in (1), when the user ofthe machine installation wants diagnosis of the presence or absence ofan anomaly in the sliding member used with the machine installation, theuser may transmit the sound or vibration data at the use point of thesliding member on the machine installation required for the anomalydiagnosis, the information identifying the sliding member, the slidingmember use condition information, etc., from the user informationprocessing terminal through the network to the diagnosis processingserver. To make a diagnosis request, if the information of the useconditions of the sliding member and the like sent by the user to thediagnosis processing server is once prepared and stored in the userinformation processing terminal, it can be used repeatedly, similarinformation need not be prepared from the beginning each time an anomalydiagnosis request is made, and the burden required for preparinginformation required for making an anomaly diagnosis request can belightened drastically. Further, the requested diagnosis processing isexecuted promptly within the scope of the information processingperformance of the diagnosis processing server 1, so that the user canget the diagnosis result early.

Therefore, if the user does not have a dedicated analytical instrumentor a skill required for anomaly diagnosis of the sliding member, theuser can make an anomaly diagnosis request easily with a small burdenand moreover can get the diagnosis result promptly and deal withoccurrence of the anomaly rapidly.

According to the anomaly diagnosis apparatus of the machine installationof the invention described in (2), to diagnose the presence or absenceof an anomaly in the machine installation, the diagnosis processing ofthe diagnosis program is performed in the information processingterminal installed in the user, so that the user is saved from having totransmit the actual measurement vibration data recording sound orvibration produced by the machine installation to be diagnosed to themanufacturer, and as labor and time required for transmitting the actualmeasurement vibration data to the manufacturer are saved, the diagnosisprocessing can be speeded up.

The diagnosis processing server to which the actual measurement dataanalysis program, the determination program, and the determinationcriterion data required for the diagnosis processing are uploaded isused to download the programs and the determination criterion data anddoes not execute the diagnosis processing itself and thereforeconcentrating of the diagnosis processing of a large number of users onone information processing apparatus can be circumvented. Further, theactual measurement data analysis program, the determination program, andthe determination criterion data required for the diagnosis processingare downloaded into the information processing terminal of the user andcan be introduced into any desired information processing terminal ofthe user if the information processing terminal has a predeterminedcommunication function and program execution performance, and thediagnosis processing can be left to any idle information processingapparatus of the user.

Therefore, as the diagnosis processing is started promptly, it can alsobe speeded up.

Further, concentrating of the diagnosis processing of a large number ofusers on one information processing apparatus need not be considered asdescribed early, so that it can be expected that even an informationprocessing apparatus having a not so high computation processingcapability will perform comparatively rapid processing.

Therefore, as a system configuration limiting the computation processingcapability of the information processing terminal in moderation in sucha manner that a popularly priced personal computer is adopted as theinformation processing terminal, the system construction cost can besuppressed to a low cost and at the same time, the diagnosis processingcan be speeded up.

In the anomaly diagnosis apparatus of the machine installation describedin (3), the Internet already constructed as a wide-area network and alsopromoted to broadband for realizing high-speed communications is used asthe network for downloading, so that there is no extra cost forconstructing, improving, etc., a dedicated network and it is madepossible for a large number of users to use the diagnosis system easilyand at low cost.

In the anomaly diagnosis apparatus of the machine installation describedin (4), it is made possible for the diagnosis processing server tomanage the versions, etc., of the diagnosis program and thedetermination criterion data required by the user in more detail withthe authentication program and the customer data in association witheach other, for example; it is made possible to realize reliabledownloading the optimum diagnosis program and the determinationcriterion data without placing any burden on the user accessing thediagnosis processing server, and service as the manufacturer can beenhanced.

Further, in the anomaly diagnosis apparatus of the machine installationdescribed in (5), illegal repeated use or drain of the diagnosisprocessing program and the determination criterion data downloaded intothe user can be prevented and erroneous use of the program and the datafor anomaly diagnosis of a different type of machine installation or thelike can be prevented.

Therefore, illegal drain of the technology of the manufacturer can beprevented and the reliability of the diagnosis processing can beenhanced.

In the anomaly diagnosis methods described in (6) and (7), anomalydiagnosis is made based only on the frequency component caused by thesliding member used with the machine installation, so that thecalculation load is lightened and the loss of the time required foranalysis can be lessened. The effect of noise and the peak of thefrequency component not caused by the sliding member of the machineinstallation can be lessened and further if the level of the frequencycomponent caused by the sliding member of the machine installation issmall (if the peak level of all spectrum is small), the frequencycomponent is reliably captured, so that higher-accuracy diagnosis ismade possible.

In the anomaly diagnosis method described in any of (8) to (10), onlythe peak value of actual measurement data is extracted, wherebyextracting of a valley point (value) of the spectrum simply because thespectrum level is high can be prevented, and higher-accuracy diagnosisis made possible.

Further, in the anomaly diagnosis method described in (10), the range ofthe time waveform after AD conversion and the spectrum waveform afterfrequency analysis is specified, whereby even for noisy sound,non-stationary sound, etc., precise selection of an abnormal soundportion is made possible and higher-accuracy diagnosis is made possible.

In the anomaly diagnosis method described in to (13), the basicfrequency component comparison process of checking whether or not thefrequency at an appearance point of a peak equal to or higher than areference level on the actual measurement frequency spectrum datamatches the basic frequency at which a peak appears because of ananomaly in a specific part of the sliding member, etc., is executed. Ifthe frequency at the appearance point of the peak equal to or higherthan the reference level on the actual measurement frequency spectrumdata matches the basic frequency in the basic frequency componentcomparison process, subsequently the low-frequency component comparisonprocess and the harmonic component comparison process are executed.

If the low-frequency component comparison process and the harmoniccomponent comparison process are executed, whether or not the peak equalto or higher than the reference level in the basic frequency on theactual measurement frequency spectrum data is caused by any other factorof overlap of frequency components of rotation components, etc., of thesliding member, etc., the effect of harmonic, etc., for example, ratherthan an anomaly of damage, etc., in the sliding member.

Thus, when the frequency at the appearance point of the peak equal to orhigher than the reference level on the actual measurement frequencyspectrum data matches the basic frequency in the basic frequencycomponent comparison process, further the low-frequency componentcomparison process and the harmonic component comparison process areexecuted, whereby erroneous diagnosis of assuming that the peak causedby any other factor of overlap of frequency components of rotationcomponents, etc., of the sliding member, etc., the effect of harmonic,etc., is caused by an anomaly in the sliding member, etc., can becircumvented and the reliability of diagnosing the presence or absenceof an anomaly in the sliding member, etc., can be improved.

According to the anomaly diagnosis method and apparatus of the machineinstallation described in (14) and (15), the comparison process ofchecking the presence or absence of a peak on the actual measurementfrequency spectrum data corresponding to frequency components occurringwhen each specific part of the sliding member of the machineinstallation is abnormal is limited to three times of the first-order,second-order, and fourth-order values of the frequency componentsoccurring when the specific part of the sliding member of the machineinstallation is abnormal and therefore the computation processing amountin the comparison process is drastically decreased as compared with therelated art case where the comparison process is repeated for all of alarge number of frequency components of first-order to high-orderfrequency components, for example.

Thus, the load on the computation processing means in analyzing thevibration signal detected from the sliding member of the machineinstallation is lightened drastically and the diagnosis work can bespeeded up. Since the computation processing amount is decreased, it ismade possible to use an inexpensive computer having a low computationprocessing capability as the computer used as the computation processingmeans and it is also made possible to decrease the apparatus cost.

Further, if a determination is made based only on the first-ordercomponent of frequency components occurring under abnormal condition,there is a possibility of making an erroneous diagnosis as a peak on thecorresponding actual measurement frequency spectrum happens to shift orgrow due to the effect of noise, etc.

However, to execute the comparison process three times of thefirst-order, second-order, and fourth-order values of the frequencycomponents occurring when the specific part of the sliding member of themachine installation is abnormal as described above, there is almost noprobability that the process will receive the effect of noise, etc.,three times, and the reliability of the diagnosis can be improved.

In doing as described in (16) and (17), if an extraction process ofextracting effective peaks based on the threshold value is performed,for example, before a computation process for comparison for the peakson the actual measurement frequency spectrum data corresponding to thefirst-order value, the second-order value, and the fourth-order value ofthe frequency components occurring when the specific part of the slidingmember of the machine installation is abnormal is executed, waste ofexecuting the comparison process for insignificant peaks can be avoidedand the load of the computation processing amount is furthermorelightened and speeding up the diagnosis process can be promoted.

Generally, growing of the peak level on the actual measurement frequencyspectrum caused by damage to the sliding member of the machineinstallation becomes most noticeable at the peak corresponding to thefirst-order value of the frequency components caused by the anomaly.

Thus, as in the anomaly diagnosis method of the machine installationdescribed in (18), the level difference between the level on the actualmeasurement frequency spectrum data corresponding to the first-ordervalue of the frequency components occurring when the specific part ofthe sliding member of the machine installation is abnormal and theeffective value or average value of the actual measurement frequencyspectrum data is calculated, whereby the magnitude of the damage can beestimated efficiently by performing minimum computation processing, andthe damaged part replacement time is determined from the estimatedmagnitude of the damage, so that excessive parts replacement andmaintenance are circumvented and it is made possible to reduce theupkeep cost in the machine and the installation containing the slidingmember of the machine installation.

According to the anomaly diagnosis apparatus of the machine installationof the invention described in (19), the sampling means automaticallyexecutes removal of the noise component from the actual measurement datadetected by the vibration detection means from the machine installationcontaining the sliding member based on the reference value set in thesampling reference setting means.

Therefore, the person in charge of diagnosis for managing the anomalydiagnosis apparatus of the machine installation need not check theactual measurement data to remove the noise component each time, and thenecessity for interrupting processing of the anomaly diagnosis apparatusof the machine installation to check the actual measurement data doesnot occur either.

That is, it is not necessary to interrupt processing of the anomalydiagnosis apparatus of the machine installation to remove the noisecomponent from the actual measurement data detected from the machineinstallation containing the sliding member, and the diagnosis processingcan be speeded up.

Further, the noise component is removed uniformly by machine processingbased on the reference value, so that the skill degree of the person incharge of diagnosis does not affect the noise component removal rate.Therefore, the noise component removal rate can be made constant and thereliability of the diagnosis can be stabilized.

Since the person in charge of diagnosis need not check the actualmeasurement data to remove the noise component, an output unit fordisplaying the actual measurement data detected by the vibrationdetection means in such a manner that the person in charge of diagnosiscan check the actual measurement data can be omitted. As the output unitis omitted, the configuration of the apparatus can be simplified and theapparatus cost can also be decreased.

1. (canceled)
 2. An anomaly diagnosis apparatus of a machineinstallation wherein an actual measurement data analysis program foranalyzing actual measurement vibration data recording sound or vibrationproduced when a sliding member used with the machine installationoperates, determination criterion data recording information used as adetermination criterion of the presence or absence of an anomaly in thesliding member used with the machine installation, and a determinationprogram for comparing the analysis result of the actual measurement dataanalysis program with the determination criterion data and diagnosingthe presence or absence of an anomaly in the sliding member arepreviously uploaded to a diagnosis processing server connected to anetwork in an executable data format in an information processingterminal of a user using the machine installation so as to enable theprograms and the data to be downloaded into the user informationprocessing terminal, characterized in that the user of the machineinstallation downloads the actual measurement data analysis program, thedetermination program, and the determination criterion data through thenetwork into the user's information processing terminal, inputs theactual measurement vibration data through an interface to the user'sinformation processing terminal whenever necessary, and executes theactual measurement data analysis program and the determination programin the user's information processing terminal for diagnosing thepresence or absence of an anomaly in the sliding member used with themachine installation in the user's information processing terminal. 3.The anomaly diagnosis apparatus of the machine installation as claimedin claim 2 wherein the Internet is used as the network.
 4. The anomalydiagnosis apparatus of the machine installation as claimed in claim 2wherein an authentication program for comparing with customerinformation data of information required for authenticating the userusing the machine installation and allowing the user to download theactual measurement data analysis program, the determination program, andthe determination criterion data when authenticating the user accessingthe diagnosis processing server as the authorized user is built in thediagnosis processing server.
 5. The anomaly diagnosis apparatus of themachine installation as claimed in any of claim 2 wherein a protectprogram for limiting the number of use times or the expiration date inthe downloading information processing terminal is added to the programsand the data downloaded from the diagnosis processing server into theuser information processing terminal. 6-25. (canceled)